This study compared the historical simulations and future projections of precipitation and temperature of Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs) to ...quantify the differences in the projections due to differences in scenarios. Five performance indicators were used to quantify the model reproducibility of the observed precipitation levels at 22 stations for the historical period of 1970–2005. The percentages of change in precipitation and temperature were estimated for the near (2025–2060) and far future (2065–2100) for two Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios of CMIP5 and two Shared Socioeconomic Pathway (SSP)2–4.5 and SSP5‐8.5 scenarios of CMIP6. The uncertainty in the projection in each case was calculated using the reliability ensemble average (REA) method. As a result, the CMIP6 GCMs showed an improvement compared with the CMIP5 GCMs with regard to the ability to simulate the historical climate. The uncertainty in the precipitation projections was higher for SSPs than that in RCPs. With regard to the temperature, the uncertainty was higher for RCPs than for SSPs. The ensemble means of the precipitation and temperature showed higher changes in the far future compared with the near future for both RCPs and SSPs. This study contributes to improvement in the confidence of future projections using CMIP6 GCMs and bolsters our understanding of the relative uncertainty in SSPs and RCPs.
Uncertainty in projected precipitation and temperature levels in South Korea for the SSP and RCP scenarios and two future periods.
The performances to reproduce the historical climate were evaluated
The future climate precipitation and temperature from CMIP5 and CMIP6 were projected
The uncertainty was quantified using reliability ensemble averaging method
This study quantified the uncertainties of future drought projections in the South Korea case by means of the reliability ensemble averaging (REA) of 10 GCM equivalents of the Coupled Model ...Intercomparison Project, Phases 5 and 6 (CMIP5 and CMIP6). Two meteorological drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI) for Representative Concentration Pathway (RCP) 4.5 and RCP8.5 of CMIP5 and Shared Socioeconomic Pathway (SSP) 2‐4.5 and SSP5‐8.5 of CMIP6 were considered. The GCMs' performances for the historical period were evaluated, and their biases were corrected using quantile mapping. The multi‐model ensemble (MME) means of the GCMs were generated using the entropy and TOPSIS methods. The results showed that the TOPSIS MME estimated more intense droughts than the entropy MME. The levels of SPI and SPEI severity estimated using the entropy MME were higher than those from the TOPSIS MME. The SPI and SPEI outcomes of RCP4.5 were much more robust than those of SSP2‐4.5. The projected drought severity in the near future was much greater than in the far future, while the reliability of drought projections in the far future was much higher than in the near future. The reliability levels of drought projections for the SSP scenarios were higher than for the RCPs for most durations. This study can support planning and management of future droughts considering uncertainty variables.
This study quantified the uncertainties of future drought projections over Korea.
Multi‐model ensemble means of GCMs were generated using the entropy and TOPSIS.
The reliability in the far future was much higher than in near future.
Uncertainty in SPEI projected droughts over South Korea for the SSP and RCP scenarios.
Flammulina velutipes is a fungus with health and medicinal benefits that has been used for consumption and cultivation in East Asia. F. velutipes is also known to degrade lignocellulose and produce ...ethanol. The overlapping interests of mushroom production and wood bioconversion make F. velutipes an attractive new model for fungal wood related studies. Here, we present the complete sequence of the F. velutipes genome. This is the first sequenced genome for a commercially produced edible mushroom that also degrades wood. The 35.6-Mb genome contained 12,218 predicted protein-encoding genes and 287 tRNA genes assembled into 11 scaffolds corresponding with the 11 chromosomes of strain KACC42780. The 88.4-kb mitochondrial genome contained 35 genes. Well-developed wood degrading machinery with strong potential for lignin degradation (69 auxiliary activities, formerly FOLymes) and carbohydrate degradation (392 CAZymes), along with 58 alcohol dehydrogenase genes were highly expressed in the mycelium, demonstrating the potential application of this organism to bioethanol production. Thus, the newly uncovered wood degrading capacity and sequential nature of this process in F. velutipes, offer interesting possibilities for more detailed studies on either lignin or (hemi-) cellulose degradation in complex wood substrates. The mutual interest in wood degradation by the mushroom industry and (ligno-)cellulose biomass related industries further increase the significance of F. velutipes as a new model.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study compared the performance of Long Short-Term Memory networks (LSTM) and Soil Water Assessment Tool (SWAT) in simulating observed runoff and projecting future runoff using 11 CMIP6 GCMs. The ...projected runoff was estimated for two Shared Socioeconomic Pathways (SSPs), 2–4.5 and 5–8.5 for near (2021–2060) and far (2061–2100) futures, respectively. The biases in GCM simulated climate variables were corrected using quantile mapping considering observations at 6 weather stations as reference data over the historical period (1985–2014). Five evaluation metrics were used to quantify the GCM's and hydrological models' capability to reconstruct climate variables and runoff in the Yeongsan Basin of South Korea. Uncertainties in LSTM and SWAT simulated runoff for the historical and projected periods were quantified using Bayesian Model Averaging (BMA) and reliability ensemble averaging (REA), respectively. The results showed significant improvement in bias-corrected GCMs in replicating observations in terms of all evaluation metrics. The extreme runoff estimated using general extreme value (GEV) distribution revealed the better capability of LSTM than SWAT in reproducing observed runoff at all gauging locations. The SWAT projected an increase (17.7%) while LSTM projected a decrease (−13.6%) in the future runoff for both SSPs at most locations. The uncertainty in LSTM simulated runoff was lower than in SWAT runoff at all stations for the historical period. However, the uncertainty in SWAT projected runoff was lower than LSTM projected runoff for both SSPs. This study helps assessing the ability of deep-learning versus physically-based models in hydrological modeling and therefore opens new perspectives for hydrological modeling applications.
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•The performance of LSTM and SWAT in runoff simulation was compared.•The runoff of SWAT was more sensitive to precipitation variation than LSTM.•SWAT underestimated the extremes while LSTM showed good performance.•SWAT projected an increase while LSTM a decrease in the future runoff.
This study compared the historical and future simulations of precipitation in South Korea from INM-CM4 of Coupled Model Intercomparison Project (CMIP) 5 and INM-CM5 of CMIP6 to identify their ...differences for the projections of corresponding scenarios by three timeframes (annual, summer and winter) and four regions (NW, NE, SW and SE). Six performance indicators were used to quantify the models' reproducibility to precipitation at 22 stations in South Korea for the historical period (1970–2005). Then, the change rates of precipitations in near and far futures (2020–2059 and 2060–2099) were calculated for two representative concentration pathway (RCP) 4.5 and 8.5 and socioeconomic shared pathway (SSP) 2–4.5 and 5–8.5. Their uncertainties were also quantified using standard deviations and interquartile ranges. As a result, CM5 clearly showed a 7.4% improvement in six performance indicators. The change rates in far future were larger but the uncertainties were smaller. But both the rates and uncertainties in NW were the largest. Also, the uncertainties in INM-CM5 were also smaller than in INM-CM4 for all timeframes and the differences between RCP4.5 and SSP2-4.5 were absolutely larger than those between RCP8.5 and SSP5-8.5.
•The historical performances and future projections of precipitation of INM-CM4 and INM-CM5 were compared.•RCP4.5, RCP8.5, SSP2-4.5 and SSP5.8.5 were used.•The change rates of precipitations were calculated over South Korea.•The percentages of change of precipitations in far future showed larger but their uncertainties were smaller.•The uncertainties in INM-CM5 were smaller than in INM-CM4.
This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical ...and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.
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•The 11 CMIP6 GCM showed good performance in climate variables.•The projected future climate variables would gradually increase in SSP scenarios.•The change in HS has shown an evapotranspiration paradox in the future period.•On the other hand, the changes in TW and PM increase in the future period.•The PM and TW have lower uncertainty than HS.
More active electrocatalysts for H2 and O2 evolution reactions, efficient membranes, and robust porous transport layers (PTL) are required for designing advanced proton exchange membrane water ...electrolysis (PEMWE) systems. An N‐doped carbon matrix is introduced in this study to surpass the existing Ti PTLs. One‐step pyrolysis results in the carbonization of polyaniline films to the N‐doped carbon matrix, simultaneous formation of desiccation cracks and IrxRuy nanoparticles, and partial impregnation of the synthesized particles into the carbon matrix. The embedded IrxRuy nanoparticles are firmly bound to the surface of the carbon matrix, inhibiting the dissolution and detachment of the nanoparticles during the O2 evolution reaction (OER). The cracks in the carbon matrix allow the steady transport of the produced O2, comparable to conventional PTLs. After optimizing the Ir and Ru contents of the nanoparticles based on the electrocatalytic performance, Ir88Ru12 embedded in the N‐doped carbon matrix is found to be the most suitable catalyst for enhancing the OER performance of the PEMWE system with negligible degradation. These findings can potentially contribute to the industrial application of PEMWE. Relevant electrochemical systems with membrane electrode assemblies, such as fuel cells and CO2 reduction systems, can be modified using the suggested structure.
Carbon matrix (CM) with desiccation cracks serves as a porous transport layer for proton exchange membrane water electrolysis. Ir‐Ru nanoparticles are embedded in the N‐doped CM, and this unique structure enhances the performance and stability of Ir‐Ru nanoparticles for oxygen evolution reaction.
This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of ...Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.
Plain Language Summary
Climate change is already contributing to various unpredictable phenomena in many fields. A well‐known organization that periodically evaluates the impacts of climate change and actionable response strategies, the IPCC assessment report states that climate change is already directly impacting ecosystems, water cycles, and human activities. Therefore, sufficient exploration of the future climate change is vital for systematically developing a plan for climate change mitigation and adaptation, and the Shared Socioeconomic Pathway scenario contains various factors such as social, economic, and physics, making it reasonable for projecting the future climate. This study evaluated the historical monthly temperature and precipitation reproducibility of CMIP6 General Circulation Model (GCM) using various metrics. Based on this, multi‐model ensemble was built using Technique for Order of Preference by Similarity to Ideal Solution, a multi‐criteria decision‐making technique, for a reasonable future climate assessment. The results of this study showed that the monthly precipitation of CMIP6 GCM over the historical period is overestimated than the reference data, but the monthly temperature performance is stable. For projected future climate, high latitudes in the northern hemisphere are most vulnerable to changes in temperature and precipitation, and the southern hemisphere captured robust dryness for the future.
Key Points
General Circulation Models' performances are different by each latitude and their simulations were overestimated for rainfall and well‐estimated for temperature
The region in N75‐N90 would be most vulnerable to climate change in the future, and the area in S30‐S60 would be drier in the future
Variability of the northern hemisphere would increase more for high emission scenarios but seasonal trends are more chaotic than in the past
This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative ...Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources.
Objectives
To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and ...angiogenesis properties on magnetic resonance imaging (MRI).
Methods
This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived.
Results
Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (
p
< 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (
p
< 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (
V
e
).
Conclusions
Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer.
Key Points
•
Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer.
•
The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network).
•
The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (V
e
) among perfusion parameters.
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