气候变化领域集成服务门户(CCPortal): Bayesian Estimation of Advanced Warning Time of Precipitation Emergence

CCPortal

DOI	10.1029/2023EF004079
	Bayesian Estimation of Advanced Warning Time of Precipitation Emergence
	Lickley, Megan; Fletcher, Sarah
发表日期	2024
EISSN	2328-4277
起始页码	12
结束页码	2
卷号	12 期号:2
英文摘要	Climate models disagree on the direction of precipitation change over about half of the Earth. Current characterizations of expected change use the ensemble mean, which systematically underestimates the magnitude and overestimates the time of emergence (ToE) of precipitation change in regions of high uncertainty. We develop a new approach to estimate both ToE and the potential to update uncertainty in precipitation over time with new observations. Further, we develop two new metrics that increase the usefulness of ToE for adaptation planning. The time of confidence estimates when projections of precipitation emergence will have high confidence. Second, the advance warning time (AWT) indicates how long policymakers will have to prepare for a new precipitation regime after they know change is likely to occur. Our approach uses individual model projections that show change before averaging across models to calculate ToE. It then applies a Bayesian method to constrain uncertainty from climate model ensembles using a perfect model approach. Results demonstrate the potential for widespread and decades-earlier precipitation emergence, with potential for end-of-century emergence to occur across 99% of the Earth compared to 60% in previous estimates. Our method reduces uncertainty in the direction of change across 8% of the globe. We find positive estimates of AWT across most of the Earth; however, in 34% of regions there is potential for no advanced warning before new precipitation regimes emerge. These estimates can guide adaptation planning, reducing the risk that policymakers are unprepared for precipitation changes that occur earlier than expected. Understanding if and when precipitation will change in response to anthropogenic warming is needed for policymakers to design adaptation plans. However, climate model projections of precipitation are highly uncertain, with models disagreeing on the direction of change across about half of Earth's surface. We develop a methodology for estimating when we expect future precipitation change to be known with a high degree of confidence. We estimate the time of emergence of new precipitation regimes on individual climate models. We also estimate how much advance warning time policymakers will have between learning that precipitation will likely change and the onset of such change. We demonstrate that precipitation change could be more widespread and sooner than previously expected, but that most regions will have advance warning. Together, our findings provide information that policymakers can use to more effectively adapt to climate change before impacts occur. We develop a method to reduce uncertainty in precipitation change by integrating observations If precipitation will change, emergence will be sooner and more widespread than previous estimates In most regions, we expect to have advanced warning time of precipitation emergence
英文关键词	time of emergence; uncertainty reduction; precipitation trends; Bayesian inference; time of confidence; advanced warning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001153012600001
来源期刊	EARTHS FUTURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/306237
作者单位	Georgetown University; Georgetown University; Stanford University; Stanford University
推荐引用方式 GB/T 7714	Lickley, Megan,Fletcher, Sarah. Bayesian Estimation of Advanced Warning Time of Precipitation Emergence[J],2024,12(2).
APA	Lickley, Megan,&Fletcher, Sarah.(2024).Bayesian Estimation of Advanced Warning Time of Precipitation Emergence.EARTHS FUTURE,12(2).
MLA	Lickley, Megan,et al."Bayesian Estimation of Advanced Warning Time of Precipitation Emergence".EARTHS FUTURE 12.2(2024).