Simulation and Modeling of Climate: Recent Trends, Current Progress and Future Directions

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (8 May 2023) | Viewed by 7377

Special Issue Editors

Lawrence Livermore National Lab, Atmosphere, Earth & Energy Division, 7000 East Avenue, Livermore, CA 94550, USA
Interests: earth system model development and application; decadal prediction; sub-grid orographic parameterization; land–atmosphere coupling

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Guest Editor
Pacific Northwest National Lab, Atmospheric Sciences and Global Change Division, 902 Battelle Blvd, Richland, WA 99354, USA
Interests: climate model development and evaluation; aerosol, cloud and precipitation processes; objective analysis of field observations
Special Issues, Collections and Topics in MDPI journals
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08544, USA
Interests: land surface modeling; hydrology; climate modeling
Lawrence Livermore National Lab, Atmosphere, Earth & Energy Division, 7000 East Avenue, Livermore, CA 94550, USA
Interests: cloud feedback and climate sensitivity; cloud parameterization; climate change

Special Issue Information

Dear Colleagues,

General circulation models (GCMs) or Earth system models (ESMs) are important tools to understand how the climate has changed in the past and may change in the future. These models use mathematical equations to describe the behavior of factors and processes that impact climate and simulate the interactions between important drivers of the climate—including atmosphere, oceans, land surface and ice—with different levels of detail. Despite their progress over the past few decades, the most up-to-date Coupled Model Intercomparison Project (CMIP6) shows that the models still exhibit large uncertainties in climate simulations and projections. Tremendous efforts are being conducted to improve climate model simulations through improvements of dynamical core, more sophisticated physical parameterizations, better coupling between different climate components, constraints from observations and increasing model resolution. As the rapid development of computer sciences quickly expands to many other fields in recent years, some studies are leveraging machine learning (ML) for to aid in understanding climate patterns and improving simulations. With these progresses in the climate modeling and simulation community, more efforts are needed to analyze the trends and progress in the current climate simulation and modeling, and to identify potential future directions.

In this Special Issue, we focus on the recent trends, current progress, and future directions of climate modeling and simulation. Topics in this Special Issue include but are not limited to those outlined below:

  • development of climate models, including dynamic core, physical parameterizations and more
  • improving predictability of the earth system by machine learning
  • model simulations, evaluation, analysis and benchmarking
  • uncertainty quantification
  • evaluation of regional or global simulations using in situ or remote-sensing observations
  • regional climate change
  • coupling of different climate components (e.g., land–atmosphere coupling, air–sea interaction) and their climate impacts
  • evaluation of the cmip6 simulations and their improvement from the previous cmips

Dr. Jinbo Xie
Dr. Shuaiqi Tang
Dr. Yujin Zeng
Dr. Yi Qin
Guest Editors

Manuscript Submission Information

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Keywords

  • model development
  • machine learning
  • model simulations, evaluation, analysis and benchmarking
  • uncertainty quantification
  • regional/global simulation evaluation
  • climate change
  • CMIP/CMIP6

Published Papers (3 papers)

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Research

16 pages, 10469 KiB  
Article
Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic
by Ziqiang Yao, Tao Zhang, Li Wu, Xiaoying Wang and Jianqiang Huang
Atmosphere 2023, 14(4), 658; https://doi.org/10.3390/atmos14040658 - 31 Mar 2023
Viewed by 1685
Abstract
Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited [...] Read more.
Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW. Full article
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12 pages, 11501 KiB  
Article
Impacts of Observed Extreme Antarctic Sea Ice Conditions on the Southern Hemisphere Atmosphere
by Zhu Zhu and Mirong Song
Atmosphere 2023, 14(1), 36; https://doi.org/10.3390/atmos14010036 - 24 Dec 2022
Cited by 3 | Viewed by 1749
Abstract
The Antarctic sea ice has undergone dramatic changes in recent years, with the highest recorded sea ice extent in 2014 and the lowest in 2017. We investigated the impacts of the observed changes in these two extremes of Antarctic sea ice conditions on [...] Read more.
The Antarctic sea ice has undergone dramatic changes in recent years, with the highest recorded sea ice extent in 2014 and the lowest in 2017. We investigated the impacts of the observed changes in these two extremes of Antarctic sea ice conditions on the atmospheric circulation in the Southern Hemisphere. We conducted three numerical simulations with different seasonal cycles of Antarctic sea ice forcings using the Community Atmosphere Model Version 5: the maximum sea ice extent in 2014 (ICE_14), the minimum sea ice extent in 2017 (ICE_17), and the average sea ice extent between 1981 and 2010 (ICE_clm, reference simulation). Our results suggest that the atmospheric response in the Southern Hemisphere showed strong seasonal variations and the atmospheric circulation in winter was more sensitive to the decreased Antarctic sea ice in 2017 than the increased sea ice in 2014. In ICE_14, the westerlies over the polar region were enhanced in summer, but there was no significant change in the zonal-averaged wind in winter. In contrast, in ICE_17, there was a clear equatorward shift in the subtropical jet in winter, but no significant change in summer. The temperature responses were limited to the Antarctic coast, where there were changes in the sea ice in ICE_14 and ICE_17. The warming on the coast of the Amundsen Sea in summer led to a slight increase in precipitation in both simulations. Full article
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17 pages, 3871 KiB  
Article
Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model
by Dali Wu, Li Wu, Tao Zhang, Wenxuan Zhang, Jianqiang Huang and Xiaoying Wang
Atmosphere 2022, 13(12), 1963; https://doi.org/10.3390/atmos13121963 - 24 Nov 2022
Cited by 3 | Viewed by 2893
Abstract
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced [...] Read more.
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively. Full article
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