Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2

Gordon, Emily M; Barnes, Elizabeth A.

doi:10.1029/2022gl098635

Cited by 20 publications

(28 citation statements)

References 79 publications

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“…We provide an example of this in Figure S5 of Supporting Information , where we include the time tendency of SST as a second input variable for the North Atlantic multi‐year prediction example. Including sea surface height or ocean heat content as an additional variable (e.g., Ding et al., 2018; Gordon & Barnes, 2022) has the potential to improve prediction skill in the North Atlantic and tropical Pacific and would provide a unique mask for where these variables provide information beyond SST alone.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Rader,

Barnes

2023

Geophysical Research Letters

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Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.

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Section: Discussionmentioning

confidence: 99%

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Rader,

Barnes

2023

Geophysical Research Letters

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“…Following recent work showing that ML methods can effectively isolate internally generated and externally forced trends (Barnes et al., 2019; Connolly et al., 2023; Gordon & Barnes, 2022; Po‐Chedley et al., 2022), we create ML algorithms to isolate these trend contributions in observed surface air temperature during the 43‐year period from 1980 to 2022 over both the Arctic and globe. To do this, we create a training data set based on 10 CMIP6 models, of which each have at least 10 ensemble members (Table S1 in Supporting Information ).…”

Section: Methodsmentioning

confidence: 99%

Internal Variability Increased Arctic Amplification During 1980–2022

Sweeney,

Fu,

Po‐Chedley

et al. 2023

Geophysical Research Letters

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Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA.

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“…Petersik and Dijkstra [26] applied two neural networks to estimate ENSO uncertainty and obtained forecast performance comparable to current state-of-the-art methods. Gordon and Barnes [27] used recurrent neural networks to produce probabilistic forecasts of climate uncertainty over 2-10 years. Rittler et al [28] used probabilistic machine learning techniques with normalized streams to generate conditional forecast distributions for probabilistic forecasting of weather forecasts.…”

Section: Uncertainty Quantifcationmentioning

confidence: 99%

Tropical Cyclone Intensity Probabilistic Forecasting System Based on Deep Learning

Fan

Yang

Yao

et al. 2023

International Journal of Intelligent Systems

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Tropical cyclones (TC) are one of the extreme disasters that have the most significant impact on human beings. Unfortunately, intensity forecasting of TC has been a difficult and bottleneck in weather forecasting. Recently, deep learning-based intensity forecasting of TC has shown the potential to surpass traditional methods. However, due to the Earth system’s complexity, nonlinearity, and chaotic effects, there is inherent uncertainty in weather forecasting. Besides, previous studies have not quantified the uncertainty, which is necessary for decision-making and risk assessment. This study proposes an intelligent system based on deep learning, PTCIF, to quantify this uncertainty based on multimodal meteorological data, which, to our knowledge, is the first study to assess the uncertainty of TC based on a deep learning approach. In this study, probabilistic forecasts are made for the intensity of 6–24 hours. Experimental results show that our proposed method is comparable to the forecast performance of weather forecast centers in terms of deterministic forecasts. Moreover, reliable prediction intervals and probabilistic forecasts can be obtained, which is vital for disaster warning and is expected to be a complement to operational models.

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Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2

Cited by 20 publications

References 79 publications

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Internal Variability Increased Arctic Amplification During 1980–2022

Tropical Cyclone Intensity Probabilistic Forecasting System Based on Deep Learning

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