In a paper published in the Journal of Geophysical Research: Atmospheres, UC Irvine Department of Earth System Science researchers, led in collaboration with those from the University of Michigan Department of Climate and Space Sciences and Engineering, showed how one of the most commonly used geoscientist climate models overestimates a key physical property of Earth’s climate system: albedo.
“We found that with the old model versions, the ice ends up being too reflective by about five percent,” said Chloe Clarke, a project scientist in UC Irvine professor Charlie Zender’s group. “The ice reflectivity was much too high.”
It’s key to estimating just how much the planet will warm in the coming years: how much sunlight the Earth receives and reflects. Previous versions of the model, called the Energy Exascale Earth System Model, or E3SM, overestimated albedo because they didn’t account for what Clarke called the microphysical properties of ice in a warming world.
Those properties include effects things like algae and dust have on albedo. Dark-colored algae and dust can render snow and ice to be less reflective and less able to reflect sunlight.
Analysis Using Satellite Data
In doing the analysis, Clarke and her team looked at satellite data to find the albedo of the Greenland Ice Sheet. They found that the E3SM reflectivity overshoots reflectivity in the ice sheet, “meaning that the model predicts less melt than would be expected from the ice microphysical properties,” said Clarke.
With the new reflectivity of ice included, the Greenland Ice Sheet is losing about six gigatons more than in older model versions. This assumes albedo measurements that are in much closer accordance with satellite observations.
Clarke hopes her team’s study underlines the importance of apparently tiny properties that make a big difference in terms of the broader climate. “I think our work is going to help models do a much better job of helping us capture snow and ice-related climate feedbacks,” she said.
Next, Clarke wants to study different icy parts of the planet to gauge how far-reaching the albedo discrepancy is in E3SM. “The next step is to get it so that it’s functional globally, and not just valid over Greenland,” said Clarke, who also intends to compare the new Greenland Ice Sheet melt rates with observations, to measure just how much more accurate the new ice albedo really is. “It would be useful to apply it to glaciers in places like the Andes and Alaska.”
In their bid to improve climate models, UC Irvine and University of Michigan researchers have unraveled some key facts about the impact of ice reflectivity-albedo-on our perception of global warming. In a new study published in the Journal of Geophysical Research: Atmospheres, they found major flaws in the Energy Exascale Earth System Model—a massively applied apparatus by geoscientists.
One key revelation concerns the fact that this model tends to overestimate ice reflectivity by some five percent. According to Chloe Clarke, a project scientist at UC Irvine, “Ice reflectivity was much too high in previous model versions.” Overestimation is very critical in implication, as it would henceforth misjudge the extent of sunlight Earth’s ice sheets reflect back into space—a very critical element of climate change impacts.
The study’s methodology relied on satellite data focused on the Greenland Ice Sheet. By comparing those observations to the output from E3SM, the scientists pointed out the discrepancies that could only originate from not taking into account microphysical ice properties in the model. This also includes the role of algae and dust darkening the ice surface and making it less reflective. Due to this fact, E3SM underrepresented the melting of ice in Greenland by approximately six gigatons compared to satellite-based measurements.
It is Clarke who puts the wider implications in a nutshell: “Our work shows how these minute properties should be considered within a climate model, in case it is to capture correct snow and ice-related climate feedbacks.” This finding comes at an invaluable moment, as scientists working on climate modeling race toward a more accurate forecast for Earth’s ice sheets responding to continued climate change.
The team plans to test its revised ice albedo model over areas other than Greenland in the future to show its global applicability. According to Clarke, “The next step will be to check our results on a global scale, not just limited to Greenland.” Such extension will consist of model outputs being compared with observational data from all icy regions around the world, including the Andes and Alaska, further refining their knowledge of the dynamics of ice melt to produce better climate projections.
Ultimately, it would be to improve the fidelity of E3SM and other climate models so that they get the complexities right about the exchanges between Earth’s surface and the atmosphere. It is through including improved parameterizations of those processes into future generations of climate models that scientists can deliver more reliable predictions needed to underpin policies for mitigating and adapting to climate change.
The study also reiterates that climate modeling is a continuous process of improvement, which is actually a precondition for its ability to keep up with new scientific understanding and data. Our models should change as our understanding of these subtleties does—for example, microphysical properties like algae or dust. This iterative process improves the prediction of ice melt rates but also helps to foresee more generally the broader impacts of climate change, including sea-level rise and changes in global weather patterns.
Such work by Clarke and colleagues underlines how inextricably linked Earth’s systems really are, and how interdisciplinary collaboration is urgently required to meet complex climate-related challenges. Connecting satellite observations with state-of-the-art modeling techniques will further enhance researchers’ understanding of how climate change is affecting ice dynamics—so imperative in formulating informed global climate adaptation and mitigation strategies.
Source: https://scitechdaily.com/
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