CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms

Model error, which results from model parameters, can cause the nonnegligible uncertainty in the North Atlantic Oscillation (NAO) simulation. Conditional nonlinear optimal perturbation related to parameter (CNOP-P) is a powerful approach to investigate the range of uncertainty caused by model parameters under a specific constraint. In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). Among 28 model parameters of the atmospheric component, the most sensitive parameter combination for the NAO+ consists of parameter for deep convection (cldfrc_dp1), minimum relative humidity for low stable clouds (cldfrc_rhminl), and the total solar irradiance (solar_const). As for the NAO−, the parameter set that can trigger the largest variation of the NAO index (NAOI) is comprised of the constant for evaporation of precip (cldwat_conke), characteristic adjustment time scale (hkconv_cmftau), and the total solar irradiance (solar_const). The most prominent uncertainties of the NAOI (ΔNAOI) caused by these two combinations achieve 2.12 for NAO+ and −2.72 for NAO−, respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for NAO+ and −1.70 for NAO−. It is indicated that the nonlinear impact of multiple parameters would be more intense than the single parameter. These results present factors that are closely related to NAO events and also provide the direction of optimizing model parameters. Moreover, the intelligence algorithms adopted in this work are proved to be adequate to explore the nonlinear interaction of parameters on the model simulation

Prediction of North Atlantic Oscillation Index with Convolutional LSTM Based on Ensemble Empirical Mode Decomposition

The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is vital to predict NAO events. Since the NAO event can be quantified by the NAO index, an effective neural network model EEMD-ConvLSTM, which is based on Convolutional Long Short-Term Memory (ConvLSTM) with Ensemble Empirical Mode Decomposition (EEMD), is proposed for NAO index prediction in this paper. EEMD is applied to preprocess NAO index data, which are issued by the National Oceanic and Atmospheric Administration (NOAA), and NAO index data are decomposed into several Intrinsic Mode Functions (IMFs). After being filtered by the energy threshold, the remaining IMFs are used to reconstruct new NAO index data as the input of ConvLSTM. In order to evaluate the performance of EEMD-ConvLSTM, six methods were selected as the benchmark, which included traditional models, machine learning algorithms, and other deep neural networks. Furthermore, we forecast the NAO index with EEMD-ConvLSTM and the Rolling Forecast (RF) and compared the results with those of Global Forecast System (GFS) and the averaging of 11 Medium Range Forecast (MRF) model ensemble members (ENSM) provided by the NOAA Climate Prediction Center. The experimental results show that EEMD-ConvLSTM not only has the highest reliability from evaluation metrics, but also can better capture the variation trend of the NAO index data

An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal

Spatio-Temporal Distribution of Dissolved Inorganic Nitrogen in the Changshan Islands Archipelago Based on a Multiple Weighted Regression Model Considering Spatial Characteristics

Ammonia nitrogen (NH4-N), nitrite nitrogen (NO2-N), and nitrate nitrogen (NO3-N) are important nutrients for maintaining the ecological balance of seawater archipelagos. Obtaining the concentrations of the three nitrogenous compounds simultaneously can allow us to comprehensively analyze nitrogen cycling in archipelago waters, which is beneficial to the ecological protection of both agriculture and fisheries. The existing studies have usually considered a single nitrogen compound or dissolved inorganic nitrogen (DIN), which can only identify the water quality but cannot comprehensively judge the water purification situation or the toxicity of the nitrogen compounds in the water. In the process of constructing an inversion model, only the specific bands of remote sensing imageries used in training/learning are directly related to the actual measured values, ignoring the fact that the specific bands contain information on water quality parameters is different that would affect the fitting accuracy. Furthermore, the existing empirical models and machine learning models have not yet been applied to high-resolution inversion in archipelago waters with active fishing activities. In view of this, we constructed a multiple weighted regression model considering spatial characteristics (S-WSVR) to simultaneously retrieve the distribution of NH4-N, NO2-N, and NO3-N in archipelagic waters. By using the S-WSVR model and considering the complexity of the spatial distribution of the three nitrogen compounds in the mesoscale archipelagic waters, longitude and latitude were added to the experimental dataset as spatial features to fit the nonlinear spatial relationships. Meanwhile, a multivariate weighting module based on the Mahalanobis distance was integrated to calculate the contribution of the characteristic bands and improve the inversion accuracy. The S-WSVR model was applied in the water of Changshan Islands, China, with a retrieval resolution of 30 m, and the r-values of the three nitrogen compounds achieved 0.9063, 0.8900, and 0.9755, respectively. Notably, the sum of the three nitrogen compounds has an r-value of 0.9028 when compared with the measured DIN. In addition, we obtained the Landsat 8 characteristic bands for the three nitrogen compounds and plotted the spatial distributions of the nitrogen compounds in spring and autumn from 2013 to 2022. By analyzing the spatio-temporal variations, it was apparent that the three nitrogen compounds are controlled by human activities and river inputs, and the anoxic discharge of the Yalu River has a strong influence on NO2-N content. Therefore, the accurate estimation in this study can provide scientific support for the protection of sensitive archipelago ecosystems

Interdecadal variability of the warm Arctic-cold Eurasia pattern linked to the Barents oscillation

Observed winter near-surface air temperature anomalies in the Northern Hemisphere have exhibited a warm Arctic-cold Eurasia (WACE) pattern with interdecadal variation in recent decades, but the exact mechanism behind WACE is still under debate. This study used reanalysis data and climate model simulations to investigate the interdecadal variability of the WACE pattern on a centennial scale, as well as the role of atmospheric circulations. It is found that the second mode of atmospheric variability over the North Atlantic-Arctic region, known as the Barents oscillation (BO), played a dominant role in regulating the interdecadal variability of WACE. The atmospheric circulation associated with the positive phase of the BO corresponds to an anomalous enhancement of the quasi-barotropic anticyclone near the southern Barents-Kara Seas (BKS) and the North Atlantic, as well as a weakening of the mid-latitude westerly jet. This atmospheric circulation anomaly favors the northward transport of atmospheric heat and moisture to the BKS from the mid-latitudes, resulting in an increased air temperature through downward longwave radiation. Concurrently cold air is transported from the polar region to Central Eurasia (CE), decreasing air temperature over CE. The amplified temperature anomaly dipole results in the decadal enhancement of the WACE pattern. The atmospheric circulation anomalies related to the negative phase of the BO are the opposite, which in turn leads to the decadal weakening of the WACE pattern. Our results further support the important role of internal atmospheric variability in the formation of WACE and emphasize that the atmospheric circulation associated with the BO is the main driver of WACE decadal variability over the past century