Long-term ocean temperature trend and marine heatwaves

Marine heatwaves(MHWs)can cause irreversible damage to marine ecosystems and livelihoods.Appropriate MHW characterization remains difficult,because the choice of a sea surface temperature(SST)temporal baseline strongly influences MHW identification.Following a recent work suggesting that there should be a communicating baseline for long-term ocean temperature trends(LTT)and MHWs,we provided an effective and quantitative solution to calculate LTT and MHWs simultaneously by using the ensemble empirical mode decomposition(EEMD)method.The long-term nonlinear trend of SST obtained by EEMD shows superiority over the traditional linear trend in that the data extension does not alter prior results.The MHWs identified from the detrended SST data exhibited low sensitivity to the baseline choice,demonstrating the robustness of our method.We also derived the total heat exposure(THE)by combining LTT and MHWs.The THE was sensitive to the fixed-period baseline choice,with a response to increasing SST that depended on the onset time of a perpetual MHW state(identified MHW days equal to the year length).Subtropical areas,the Indian Ocean,and part of the Southern Ocean were most sensitive to the long-term global warming trend.

Long-term trends in the abundance and breeding performance in Adélie penguins:the Argentine Ecosystem Monitoring Program

In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.

The burden and long-term trends of breast cancer by different menopausal status in China

Background: The burden of breast cancer in women of different menopausal status has not been assessed in China previously. We aim to evaluate and project the burden of breast cancer in different menopausal status in China. Methods: The incidence and mortality of breast cancer were estimated using the data of 554 cancer registries in 2017 and the trends of incidence and mortality of 112 cancer registries from 2010 to 2017. Data from 22 continued cancer registries from 2000 to 2017 were applied for long-term trend projection to 2030 using the Bayesian age- period-cohort model. Menopausal status was stratified by age, with premenopause defined as chronological age < 45 years, perimenopause defined as 45-54 years, and postmenopause defined as ≥ 55 years. Results: Approximately 352,300 incident cases and 74,200 deaths of breast cancer occurred in China in 2020, contributing to 2.6 million disability-adjusted life years (DALYs). Perimenopausal women had the highest inci- dence, prevalence, and DALYs rates, with the rates being 100.3 per 100,000, 819.2 per 100,000 and 723.1 per 100,000 persons. While postmenopausal women had the highest mortality rates (25.5 per 100,000 persons). From 2000 to 2017, the largest increase in incidence and mortality for breast cancer was observed in postmenopausal women with an average annual percentage change (AAPC) of 5.6% and 2.94%. The number of breast cancer cases and deaths will increase to 452,000 and 98,800 in 2030, resulting in 3.2 million DALYs. Conclusions: The burden of breast cancer is rapidly increasing in China and varies among different menopausal status. Specific prevention and control strategies for women in different menopausal status will be more helpful in reducing the rapidly growing trends of breast cancer.

The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a

Utilizing the 45 a European Centre for Medium-Range Weather Forecasts （ECMWF） reanalysis wave da- ta （ERA-40）, the long-term trend of the sea surface wind speed and （wind wave, swell, mixed wave） wave height in the global ocean at grid point 1.5°× 1.5° during the last 44 a is analyzed. It is discovered that a ma- jority of global ocean swell wave height exhibits a significant linear increasing trend （2-8 cm/decade）, the distribution of annual linear trend of the significant wave height （SWH） has good consistency with that of the swell wave height. The sea surface wind speed shows an annually linear increasing trend mainly con- centrated in the most waters of Southern Hemisphere westerlies, high latitude of the North Pacific, Indian Ocean north of 30°S, the waters near the western equatorial Pacific and low latitudes of the Atlantic waters, and the annually linear decreasing mainly in central and eastern equator of the Pacific, Juan. Fernandez Archipelago, the waters near South Georgia Island in the Atlantic waters. The linear variational distribution characteristic of the wind wave height is similar to that of the sea surface wind speed. Another find is that the swell is dominant in the mixed wave, the swell index in the central ocean is generally greater than that in the offshore, and the swell index in the eastern ocean coast is greater than that in the western ocean inshore, and in year-round hemisphere westerlies the swell index is relatively low.

Displacement Trends of Slow-moving Landslides: Classification and Forecasting

A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.

Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China

The observed long-term trends in extreme temperatures in Hong Kong were studied based on the meteorological data recorded at the Hong Kong Observatory Headquarters from 1885-2008. Results show that, over the past 124 years, the extreme daily minimum and maximum temperatures, as well as the length of the warm spell in Hong Kong, exhibit statistically significant long-term rising trends, while the length of the cold spell shows a statistically significant decreasing trend. The time-dependent return period analysis also indicated that the return period for daily minimum temperature at 4°C or lower lengthened considerably from 6 years in 1900 to over 150 years in 2000, while the return periods for daily maximum temperature reaching 35°C or above shortened drastically from 32 years in 1900 to 4.5 years in 2000. Past trends in extreme temperatures from selected weather stations in southern China from 1951-2004 were also assessed. Over 70% of the stations studied yielded a statistically significant rising trend in extreme daily minimum temperature, while the trend for extreme maximum temperatures was found to vary, with no significant trend established for the majority of stations.

Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020

Reasonably understanding of the long-term wave characteristics is very crucial for the ocean engineering.A feedforward neural network is operated for interpolating ERA5 wave reanalysis in this study,which embodies a detailed record from 1950 onwards.The spatiotemporal variability of wave parameters in the Bohai Sea,especially the significant wave height(SWH),is presented in terms of combined wave,wind wave and swell by employing the 71 years(1950–2020)of interpolated ERA5 reanalysis.Annual mean SWH decreases at−0.12 cm/a estimated by Theil-Sen estimator and 95th percentile SWH reflecting serve sea states decreases at−0.20 cm/a.Inter-seasonal analysis shows SWH of wind wave has steeper decreasing trend with higher slopes than that of swell,especially in summer and winter,showing the major decrease may attribute to the weakening of monsoon.The inner Bohai Sea reveals a general decreasing trend while the intersection connecting with the Yellow Sea has the lower significance derived by Mann-Kendall test.Meanwhile,95th percentile SWH decreases at a higher rate while with a lower significance in comparison with the mean state.The frequencies of mean wave directions in sub-sector are statistically calculated to find the seasonal prevailing directions.Generally,the dominant directions in summer and winter are south and north.A similar variation concerning to SWH,the trend of the mean wave period is provided,which also shows a decrease for decades.

Long-Term Trends in the Probability of Different Grades of Haze Days in China from 1960 to 2013

The number of haze days and daily visibility data for 543 stations in China were used to define the probabilities of four grades of haze days:slight haze(SLH)days;light haze(LIH)days;moderate haze(MOH)days;and severe haze(SEH)days.The change trends of the four grades of haze were investigated and the following results were obtained.The highest probability was obtained for SLH days(95.138%),which showed a decreasing trend over the last54 years with the fastest rate of decrease of-0.903%·(10 years)-1 and a trend coefficient of-0.699,passing the 99.9%confidence level.The probabilities of LIH and MOH days increased steadily,whereas the probability of SEH days showed a slight downward trend during that period.The increasing probability of SLH days was mainly distributed to the east of 105°E and the south of 42°N and the highest value of the trend coefficient was located in the Pearl River Delta and Yangtze River Delta regions.The increasing probability of LIH days was mainly distributed in eastern China and the southeastern coastal region.The probabilities of MOH and SEH days was similar to the probability of LIH days.An analysis of the four grades of haze days in cities with different sizes suggested that the probability of SLH days in large cities and medium cities clearly decreased during the last 54 years.However,the probabilities of LIH days was<10%and increased steadily.The probability of MOH days showed a clear interdecadal fluctuation and the probability of SEH days showed a weak upward trend.The probability of SLH days in small cities within 0.8°of large or medium cities decreased steadily,but the probability of LIH and MOH days clearly increased,which might be attributed to the impact of large and medium cities.The probability of SLH days in small cities>1.5°from a large or medium city showed an increasing trend and reached 100%after 1990;the probability of the other three grades was small and decreased significantly.

Long-Term Trends in Photosynthetically Active Radiation in Beijing

A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma’an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.

Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

Internet of Things(IoT)is a network that connects things in a special union.It embeds a physical entity through an intelligent perception system to obtain information about the component at any time.It connects various objects.IoT has the ability of information transmission,information perception,and information processing.The air quality forecasting has always been an urgent problem,which affects people’s quality of life seriously.So far,many air quality prediction algorithms have been proposed,which can be mainly classified into two categories.One is regression-based prediction,the other is deep learning-based prediction.Regression-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress themeteorological value.Deep learning methods usually use convolutional neural networks(CNN)or recurrent neural networks(RNN)to predict the meteorological value.As an excellent feature extractor,CNN has achieved good performance in many scenes.In the same way,as an efficient network for orderly data processing,RNN has also achieved good results.However,few or none of the above methods can meet the current accuracy requirements on prediction.Moreover,there is no way to pay attention to the trend monitoring of air quality data.For the sake of accurate results,this paper proposes a novel predicted-trend-based loss function(PTB),which is used to replace the loss function in RNN.At the same time,the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM_(2.5).In addition,this paper extends the model scenario to the prediction of the whole existing training data features.All the data on the next day of the model is mixed labels,which effectively realizes the prediction of all features.The experiments show that the loss function proposed in this paper is effective.

Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application

The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term,ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region.The fixed station was established in 1989,and conventional radiation observations started much later in 2008.In this study,a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data,and a highprecision,long-term DGSR dataset is constructed.Then,the trend of DGSR from 1990 to 2019 at Zhongshan Station,Antarctica is analyzed.The RF model,which performs better than other models,shows a desirable performance of DGSR hindcast estimation with an R^2 of 0.984,root-mean-square error of 1.377 MJ m^(-2),and mean absolute error of 0.828 MJ m^(-2).The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004.Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station.In addition to clouds and water vapor,bad weather conditions (such as snowfall,which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station.The high-precision,longterm estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow,ice,and atmosphere.

Study of Holocene glacier degradation in central Asia by isotopic methods for long-term forecast of climate changes

This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

foF2 Long-Term Trend at a Station Located near the Crest of the Equatorial Ionization Anomaly

Critical frequency foF2 long-term trends at Dakar station (14.4°N, 342.74°E) located near the crest of the equatorial ionization anomaly EIA, are analysed taking into account geomagnetic activity, increasing greenhouse gases concentration and Earth’s magnetic field secular variation. After filtering solar activity effect using F10.7 as a solar activity proxy, we determined the relative residual trends slopes α values for three different levels of geomagnetic activity. For example, at 1200 LT, the value of α goes from -0.27%/year for very magnetically quiet days to -0.19%/year for magnetically quiet days and to -0.13%/year for all days. It appears from the slopes α obtained, that they increase with the level of geomagnetic activity and their negative values are qualitatively consistent with the expected decreasing trend due to the increase in greenhouse gases concentration but are greater than 0.003%/year which would result from a 20% increase in CO2 emissions which actually took place during the analysis period. Regarding Earth’s magnetic field magnitude, B secular variation and the dip equator secular movement, Dakar station is located near the crest of the equatorial ionization anomaly, Earth’s magnetic field magnitude, B decreases there and the trough approaches the position of Dakar during the period of analysis. These two phenomena induce a decrease in foF2 which is in agreement with the decreasing trend observed at this station.

Long-Term Rainfall Trends in South West Asia—Saudi Arabia

In this study, rainfall data from 19 stations in Saudi Arabia (SA) for the period 1985-2019 was utilized to investigate interannual, monthly, and seasonal rainfall variations and trends. The magnitudes of these trends were characterized and tested using Mann-Kendall (MK) rank statistics at different significance levels. During this study period, the mean rainfall in SA showed a slight and significant decreasing trend by about 2 mm/35 years. Investigation of seasonal trends of rainfall revealed that Winter and Spring rainfall decreased significantly by 2.7 mm/35 years and 5.4 mm/35 years respectively. Three months showed very slight significant decreasing trends of rainfall. These were the months of February, March and April. Mann-Kendall analyses were carried out to investigate the annual trends of rainfall during three sub-periods, i.e., 1985-1996, 1997-2008, and 2009-2019. The results revealed that while rainfall increased by 5.3 mm/12 years and 7.8 mm/11 years for the first and the third periods respectively, it decreased by about 11 mm/12 years during the second period. While trends of rainfall in Saudi Arabia are affected by large scale circulations and local factors, the effect of extraterrestrial factors, such as solar activity and its consequent effects on the climate may, additionally, play a potential role in affecting the pattern of rainfall in Saudi Arabia.

Long-Term Visibility Trends in the Riyadh Megacity, Central Arabian Peninsula and Their Possible Link to Solar Activity

In this study, atmospheric visibility (AV) data from Riyadh, Saudi Arabia (24.91˚N, 46.41˚E, 760 m), for the period 1976-2011 were utilized to investigate the interannual, monthly, and seasonal AV variations and trends. The magnitudes of these trends were characterized and tested using mann-kendall (MK) rank statistics at different significance levels. No significant trend in AV was observed during the 36-year period. However, a significant increase in the annual mean AV by 0.24 km per year for the period between 1976 and 1999 was found. For the period 1999-2011, AV decreased significantly by 0.16 km per year. The potential effects of air temperature and relative humidity on AV were investigated. While these two variables could explain the observed trend of AV over some periods, they failed to do so for the whole study period. To search for extraterrestrial causes for long-term AV variations, correlation analyses between the time series of cosmic ray (CR) data (measured by NM and muon detector) and solar activity (represented by sunspot number) and AV were conducted and showed that these two variables are able to explain the AV variations for the whole study period. Additionally, power spectra analyses were conducted to investigate periodicities in the AV time series. Several significant periodicities, such as 9.8, 5.2, 2.2, 1.7, and 1.3 years were recognized. The obtained periodicities were similar to those reported by several investigators and found in solar, interplanetary, and CR parameters. The spectral and correlation results suggested that, with the expected effects of terrestrial and meteorological conditions on AV, long-term AV variations can also be related to the solar activity and associated CR modulations.

Using the Analytic Hierarchy Process in Long-Term Load Growth Forecast

The load growth is the most important uncertainties in power system planning process. The applications of the classical long-term load forecasting methods particularly applied to utilities in transition economy are insufficient and may produce incorrect decisions in power system planning process. This paper discusses using the method of analytic hierarchy process to calculate the probability distribution of load growth obtained previously by standard load forecasting methods.