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"Dong, Wenjie"
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Evaluation of extreme precipitation over Asia in CMIP6 models
Based on four reanalyses or gridded data sets (ERA5, 20CR, APHRODITE and REGEN), we provide an overview of 23 Historical and 7 HighResMIP experiments’ performance from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (for short, 6-Hist, HighRes) in simulating seven extreme precipitation indices over Asia defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We compare them with 28 Historical experiments in CMIP5 (5-Hist). CMIP5 and CMIP6 models are generally able to reproduce extreme precipitation’s spatial distribution and their trend patterns in comparison to the benchmark data set (APHRODITE). The overall performance of individual model is summarized by a “portrait” diagram based on four statistics for each index. We divide all 58 models into three groups (A, the top 20%; B, the median 60% and C group, the last 20%) according to MR rankings (the comprehensive ranking measure). Based on the “portrait” diagram and MR rankings, models that perform relatively well for all seven extreme precipitation indices include HadCM3, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES from 5-Hist, EC-Earth3, EC-Earth3-Veg from 6-Hist and ECMWF-IFS-HR, ECMWF-IFS-LR, ECMWF-IFS-MR from HighRes. The simulated performance of CMIP6 is polarized, for the top four and the last five ranking models are both from CMIP6. Compared with the counterpart models in CMIP6 and CMIP5, the improvement of PCC (pattern correlation coefficient) is more obvious. Furthermore, the dry biases of CMIP6 (both 6-Hist and HighRes) in Southern China and India and the wet biases of CMIP6 in Tibet are reduced compared to CMIP5. This may benefit from the improvement that CMIP6 models can capture the characteristics of meridional moisture flux convergence, and improve the overestimation or underestimation of meridional and zonal specific humidity eddies compared to CMIP5.
Journal Article
Historical Volcanic Eruptions Mitigated the Expected Rapid Arctic Sea Ice Decline Prior to 2000
Arctic sea ice has declined at sharply contrasting rates over the past four decades—modest before 2000 and rapid thereafter. Using observational and model evidence, we show that large tropical volcanic eruptions can trigger decade‐long Arctic sea ice recoveries, and that without the 1982 El Chichón and 1991 Pinatubo eruptions, Arctic sea ice would have declined approximately 1.5 times faster before 2000. We further show a model's sensitivity to volcanic aerosol forcing scales with its sensitivity to GHG forcing across CMIP6 models, offering a new strategy to identify models with realistic climate response to radiative forcing. Following this, a selected subgroup of models that accurately simulate long‐term warming trend and decade‐long post‐Pinatubo recovery project ice‐free Arctic summer up to 20 years earlier than the full ensemble. These findings underscore the critical, yet underappreciated, importance of evaluating climate models against anthropogenic and volcanic forcing when projecting the future of Arctic sea ice.
Journal Article
Intraseasonal variability of global land monsoon precipitation and its recent trend
Accurate prediction of global land monsoon rainfall on a sub-seasonal (2–8 weeks) time scale has become a worldwide demand. Current forecasts of weekly-mean rainfall in most monsoon regions, however, have limited skills beyond two weeks, calling for a more profound understanding of monsoon intraseasonal variability (ISV). We show that the high-frequency (HF; 8–20 days) ISV, crucial for the Week 2 and Week 3 predictions, accounts for about 53–70% of the total (8–70 days) ISV, generally dominating the sub-seasonal predictability of various land monsoons, while the low-frequency (LF; 20–70 days)’s contribution is comparable to HF only over Australia (AU; 47%), South Asia (SA; 43%), and South America (SAM; 40%). The leading modes of HFISVs in Northern Hemisphere (NH) monsoons primarily originate from different convectively coupled equatorial waves, while from mid-latitude wave trains for Southern Hemisphere (SH) monsoons and East Asian (EA) monsoon. The Madden-Julian Oscillation (MJO) directly regulates LFISVs in Asian-Australian monsoon and affects American and African monsoons by exciting Kelvin waves and mid-latitude teleconnections. During the past four decades, the HF (LF) ISVs have considerably intensified over Asian (Asian-Australian) monsoon but weakened over American (SAM) monsoon. Sub-seasonal to seasonal (S2S) prediction models exhibit higher sub-seasonal prediction skills over AU, SA, and SAM monsoons that have larger LFISV contributions than other monsoons. These results suggest an urgent need to improve the simulation of convectively coupled equatorial waves and two-way interactions between regional monsoon ISVs and mid-latitude processes and between MJO and regional monsoons, especially under the global warming scenarios.
Journal Article
A New Model and its Application to Investigate Transpiration Cooling with Liquid Coolant Phase Change
This paper presents a new mathematical model, semi-mixing model (SMM), to describe transpiration cooling with coolant phase change from liquid into vapor through two-phase process. The local heat exchange of fluid-solid within pores is considered in this model, and therefore it is closer to real transpiration cooling condition. The differences from the separated phase model and two-phase mixture model are that SMM can overcome the trouble of tracking phase change interface and avoid the inveracious numerical phenomenon, i.e., a thermal insulating layer occurs within the porous matrix. Using SMM, the corresponding numerical method is realized to simulate the processes of coolant moving, absorbing heat and evaporating within porous matrix. To validate SMM and the numerical strategy, an experiment is conducted. Using the validated SMM and numerical strategy, the effects of two-dimensional coolant injection rate and two-dimensional heat flux on transpiration cooling characteristics are simulated and analyzed. The simulations and analysis discover several interesting and valuable phenomena.
Journal Article
Diversity of intraseasonal oscillation over the western North Pacific
The western North Pacific (WNP) intraseasonal oscillation (ISO) is the strongest over the globe, its prediction is the cornerstone for subseasonal prediction of the Asian summer monsoon. Yet, our understanding of the diversity of the WNP ISO is limited, which challenges our modeling and prediction efforts. We study the diversity of observed WNP ISO by performing cluster analysis on propagation patterns of ISO events, targeting three clusters: westward, northeastward, and northwestward propagations. The westward cluster exists within the WNP, while the other two are related to the northeastward propagating rain band originated from the central equatorial Indian Ocean and to the northwestward propagating dipole across the western Pacific and Indian Ocean, respectively. Moist static energy (MSE) tendency contributing to these different propagations is mainly due to horizontal advection, while radiative heating mainly maintains the ISO’s development. Background sea surface temperature (SST) and MSE anomalies partly determine this ISO diversity, especially for those ISOs with large similarity. The westward cluster is related to warm SST anomalies in the western Indian Ocean, as a combination of seasonal cycle and internal interannual-to-interdecadal variability. The northeastward cluster is related to the cold Pacific Meridional Mode and La Niña-like pattern, while the northwestward cluster is related to the opposite warm background, both as the internal variability. Our finding of the background-affected ISO diversity over the WNP can be conducive to both model simulation and subseasonal prediction of the Asian summer monsoon.
Journal Article
Enhancing Robustness in UDC Image Restoration Through Adversarial Purification and Fine-Tuning
This study presents a novel defense framework to fortify Under-Display Camera (UDC) image restoration models against adversarial attacks, a previously underexplored vulnerability in this domain. Our research initially conducts an in-depth robustness evaluation of deep-learning-based UDC image restoration models by employing several white-box and black-box attacking methods. Following the assessment, we propose a two-stage approach integrating diffusion-based adversarial purification and efficient fine-tuning, uniquely designed to eliminate perturbations while retaining restoration fidelity. For the first time, we systematically evaluate seven state-of-the-art UDC models (such as DISCNet, UFormer, etc.) under diverse attacks (PGD, C&W, etc.), revealing severe performance degradation (DISCNet’s PSNR drops from 35.24 to 15.16 under C&W attack). Our framework demonstrates significant improvements: after purification and fine-tuning, DISCNet’s PSNR rebounds to 32.17 under PGD attack (vs. 30.17 without defense), while UFormer achieves a 19.71 PSNR under LPIPS-guided attacks (vs. 17.38 baseline). The effectiveness of our proposed approach is validated through extensive experiments, showing marked improvements in resilience against various adversarial attacks.
Journal Article
A Machine Learning Method for Predicting Vegetation Indices in China
To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.
Journal Article
Tropical volcanism enhanced the East Asian summer monsoon during the last millennium
Extreme East Asian summer monsoon (EASM) rainfall frequently induces floods that threaten millions of people, and has been generally attributed to internal climate variability. In contrast to the hydrological weakening theory of volcanic eruptions, here we present convergent empirical and modeling evidence for significant intensification of EASM rainfall in response to strong tropical volcanic eruptions. Our multi-proxy analyses show a significantly increased EASM in the first summer after tropical eruptions from 1470 AD to the present, and the more frequent occurrence of El Niños in the first boreal winter after eruptions is necessary for the enhanced EASM. Model simulation ensembles show that a volcano-induced El Niño and the associated stronger than non-volcanic El Niño warm pool air-sea interaction intensify EASM precipitation, overwhelming volcanic-induced moisture deficiency. This work sheds light on the intertwined relationship between external forcing and internal climate variability and potential flood disasters resulting from tropical volcanic eruptions.
The probability of an El Niño in the winter after large tropical volcanic eruptions increases. When this happens, summer monsoon precipitation over East Asia is enhanced, overwhelming thermodynamic precipitation reduction from volcanic cooling.
Journal Article
More profound impact of CP ENSO on Australian spring rainfall in recent decades
Most of Australia was in severe drought from 2018 to early 2020. Here we link this drought to the Pacific and Indian Ocean sea surface temperature (SST) modes associated with Central Pacific (CP) El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Over the last 20 years, the occurrence frequency of CP El Niño has increased. This study extends the previous understanding of eastern Pacific (EP) El Niño-Australian rainfall teleconnections, exhibiting that CP El Niño can bring much broader and stronger rainfall deficiencies than EP El Niño during austral spring (September–November) over the northern Australia (NAU), central inland Australia and eastern Australia (EAU). The correlations between SST fields and rainfall in three Cluster regions divided by clustering analysis also confirm this, with rainfall variability in most of Australia except southern Australia (SAU) most significantly driven by CP ENSO. Also, we demonstrate that the CP El Niño affects rainfall in extratropical EAU via the Pacific-South American (PSA) pattern. While the influence of EP El Niño is only confined in tropical NAU because its PSA pattern sits far too east to convey its variability. With the development of ENSO diversity since 2000, the footprint of El Niño on Australian rainfall has become more complex.
Journal Article