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Study Demonstrates the Importance of Data Management When Downscaling3 min read

by Joey Besl on December 12, 2022

Climate and Ecosystem Sciences Division
Mean of historical trends in June, July, and August-mean daily-maximum temperature for each climate division in California. Results are shown as a function of the start year of the historical period (which always ends in 2005) used to calculate trends.

Mean of historical trends in June, July, and August-mean daily-maximum temperature for each climate division in California. Results are shown as a function of the start year of the historical period (which always ends in 2005) used to calculate trends.

Global climate models can help show the planet’s future, but what if you want to zoom in on an individual spot on the map? To get more local information, climate scientists commonly use an approach called downscaling to make global data relevant to an individual area. Downscaling relies, in part, on comparing the observed historical relationships between local and large-scale weather to make global models relevant to the local level. But downscaling is a tricky business, even in a heavily instrumented data-rich place like California.

For example, some historical records show that coastal California had cooled over the past few decades even while the rest of the state warmed up. The contrast between cooling and warming is counterintuitive to climate trends and suggests less warming in coastal California in the future. This anomaly raised a red flag for Dan Feldman of Berkeley Lab and his colleagues at Indiana University. So they decided to investigate. The team found that while the observations of the past decades are robust, the historical records built from those observations include potentially compromised data. 

“There’s a lot of great information in those historical temperature records,” said Feldman, a staff scientist in the national lab’s Earth and Environmental Sciences Area, “but for any large long-term temperature record, the technology will change.” 

Some climate records are more than a century old, meaning the record needs to consider not just what was measured, but also how. Older weather stations may have disruptions in the data. Some datasets, especially in cities, have weather stations that moved over time to accommodate land-use changes – like a highway or housing project built nearby. Feldman and his colleagues discovered that the historical records that do carefully consider how temperature data are collected do not show coastal cooling. This finding has major implications for future projections of downscaled coastal temperatures in California. 

Their results, published in Geophysical Research Letters, show that there are major differences in the six historical datasets they looked at exactly because some datasets consider how temperature records were collected and some do not. Two of the models used homogenized data sets, which took into account how temperature records were collected. Four used non-homogenized data sets, incorporating only raw data without taking into account how temperature records were collected. Rather, evidence of coastal cooling in California only showed up in the non-homogenized data. 

The study demonstrates the importance of data management when downscaling. Simply choosing a model that worked well with historical observations could open the door for serious errors,  if you don’t also consider how the observations were collected.  The apparent, but ultimately artificial, California coastal cooling that inspired this study is one example of such an error.  By carefully looking at both the data and how it is collected, climate modelers can provide local users with more accurate information.

Homogenized data that’s appropriately downscaled will allow cities and communities to better plan for a warming climate. Likewise, uninterrupted data paint a more realistic picture of temperature trends. “There is always a range of projections for what climate conditions will be in the future, and it’s easier to look at the lower end of the range and hope that’s what’s going to happen,” said Feldman. “I think it’s harder to look at the upper end of that range and plan for that, but we have to make sure that the lower end of the range isn’t overly optimistic and the upper end of the range isn’t overly pessimistic.”

News & Events

Chun Chang Places Second in Annual Berkeley Lab Pitch Competition3 min read

January 18, 2023

Commercializing Berkeley Lab inventions is an important part of the Lab’s mission, and one that requires strong communication skills. For example, Lab inventors need to be able to pitch their ideas to external partners and potential funders.  The annual Berkeley Lab Pitch Competition occurred on October 27, 2022 and is a part of an entrepreneurship…

EESA Scientists Collaborate With Universities to bring Environmental Science Research Opportunities and Training to Students Underrepresented in STEM3 min read

January 13, 2023

  EESA researchers are collaborators in three of the 41 projects awarded in December by DOE through its Reaching a New Energy Sciences Workforce (RENEW) initiative.  RENEW aims to build foundations for research at institutions that have been historically underrepresented in the Office of Science (SC) research portfolio. The initiative provides opportunities for undergraduate and…

New Report Explores Revolutionary Environmental Artificial Intelligence Infrastructure5 min read

January 10, 2023

In a collaborative effort between the U.S. Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) and DOE’s Advanced Scientific Computing Research (ASCR) program, as well as with community experts, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held from October through December 2021. BER developed the process as the Model-Experiment paradigm, or ModEx, and a report released this fall outlines the key takeaways of last year’s event.

A Q&A With Postdoc Kunxiaoja Yuan3 min read

January 4, 2023

  Kunxiaojia Yuan received her Bachelor’s of Engineering in remote sensing and Ph.D. in geographic information engineering from Wuhan University. She is a postdoctoral researcher in EESA, with a research focus on global carbon, energy, and water cycle analysis and model evaluation using machine learning and causal inference. What motivated you to pursue a postdoc…

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