Over the past 40 years, winters in California have become drier. This is a problem for agricultural operations in the region, where farmers depend on winter rainfall to irrigate their crops. Determining whether California will continue to dry out, or whether the trend will reverse, has implications for millions of its residents.
But so far, climate models that explain changes in greenhouse gases and other human activities have had difficulty reproducing the drying trends observed in California. When climate models predict the future or simulate the past, they cannot agree on long-term trends in precipitation. Researchers at Pacific Northwest National Laboratory (PNNL) want to know why these mixed results are not very useful for future water resource planning.
“When we see these big uncertainties in model simulations and projections, we have to ask whether the models are mission ready,” said Robbie Leung, Patel’s fellow and PNNL atmospheric scientist. “One of the challenges for California modeling is that long-term natural cycles strongly influence precipitation.”
These cycles range from long years, such as El Niño and La Niña, to long decades, such as the Pacific Interdecadal Oscillation (IPO). They represent the natural variation associated with sea surface temperature patterns in the Pacific Ocean and influence California winter precipitation.
But how much of a role do they play in producing uncertainty in California’s rainfall forecast? A big one, it turns out. Results from Leung and the PNNL team show that natural cycles are responsible for more than 70 percent of the uncertainty in model simulations of rainfall trends over the past 40 years. By isolating the effects of natural cycles, scientists can focus on improving models to reduce remaining uncertainty related to how greenhouse gases and other human activities affect the climate.
With more computing power, researchers can now run large sets of simulations called large ensemble simulations. To produce it, researchers ran climate models 40 to 100 times with slight variations in starting conditions. Since everything except the starting conditions remains the same, these groups provide a unique representation of natural diversity. Modeling centers around the world also run simulations that contribute to multiple model sets. These represent the total uncertainty due to both the normal variance and uncertainty in the model.
Leung and her team analyzed three mass simulations generated by three different climate models and two multi-model ensembles for two recent generations of climate models. They wanted to identify sources of uncertainty in California’s rainfall forecast. What they found surprised them.
The team found that natural climate cycles have been responsible for nearly 70 percent of the total uncertainty in model simulations of California’s rainfall trends in the past 40 years. This leaves 30 percent of the uncertainty about how the models represent human impact on climate.
“We know that natural cycles have significant impacts on California’s climate, but we didn’t think they would dominate the overall uncertainty in climate simulations to this extent,” Leung said. “This result shows the importance of simulating large populations to isolate human influence on climate, which may be small compared to natural cycles in some regions.”
Natural Cycles vs. Human Influences
Among the natural cycles that affect California’s climate, the IPO is one of the most important. Their decades-long phases help determine if California is on a wetting or drying trend. The team’s findings point to its primary role in draining California over the past 40 years.
Currently, climate models have limited skill in predicting the transition between underwriting phases—especially decades from now. Therefore, future forecasts of California precipitation have a great deal of uncertainty due to IPO cycles.
So where does that leave human-caused changes, such as global warming and an increase in greenhouse gases? They still play a large role in shaping the climate and weather in the future. As greenhouse gases continue to build up in the atmosphere, and the large heat capacity of the ocean catches up with increasing temperatures, warming and its effects will become more pronounced.
“The natural contrast, like an IPO, is like background noise,” Leung said. “Although this noise is significant, the climate response to increasing greenhouse gas concentrations is a signal that grows over time. Focusing our efforts on reducing disagreement about this signal is impressive, especially when looking into the distant future.”
Understanding how natural and external factors influence California’s rainfall helps researchers better contextualize their forecasts. This knowledge helps designers explain why their models miss the mark in simulating trends observed in the past. The scientists can then communicate more accurate findings to people planning the future of California’s water.
Even if we stop global warming, local climates will change – and we need new experiments to understand how this happens
Lu Dong et al, Uncertainty in El Niño-like warming and California precipitation changes associated with interdecadal oscillation in the Pacific Ocean, Nature Communications (2021). DOI: 10.1038 / s41467-021-26797-5
the quote: Climate Cycles Creates Uncertainty About California Rainfall (2021, December 11) Retrieved December 11, 2021 from https://phys.org/news/2021-12-climate-california-prec-id-uncertainty.html
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