Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)

Litterfall production modeling based on climatic variables and nutrient return from stands of Eucalyptus grandis Hill ex Maiden and Pinus taeda L.

Native grasslands in the Pampas of South America are increasingly being replaced by Eucalyptus and Pinus stands.The short rotation regimes used for the stands require high nutrient levels,with litterfall being a major source of nutrient return.To model the litterfall production using climatic variables and assess the nutrient return in 14-year-old Eucalyptus grandis and Pinus taeda stands,we measured litter production over 2 years,using conical litter traps,and monitored climatic variables.Mean temperature,accumulated precipitation,and mean maximum vapor pres-sure deficit at the seasonal level influenced litterfall produc-tion by E.grandis;seasonal accumulated precipitation and mean maximum temperature affected litterfall by P.taeda.The regression tree modeling based on these climatic vari-ables had great accuracy and predictive power for E.grandis(N=33;MAE(mean absolute error)=0.65;RMSE(root mean square error)=0.91;R^(2)=0.71)and P.taeda(N=108;MAE=1.50;RMSE=1.59;R^(2)=0.72).The nutrient return followed a similar pattern to litterfall deposition,as well as the order of importance of macronutrients(E.grandis:Ca>N>K>Mg>P;P.taeda:N>Ca>K>Mg>P)and micronutrients(E.grandis and P.taeda:Mn>Fe>Zn>Cu)in both species.This study constitutes a first approximation of factors that affect litterfall and nutrient return in these systems.

Climatic implications in earlywood and latewood width indices of Chinese pine in north central China

Latewood width(LWW)indices of trees are considered a reliable proxy of summer precipitation in the Northern Hemisphere.However,the strong coupling and high correlation between earlywood width(EWW)and LWW indices often prevent registration of climate signals of the LWW index.In this study,328-year-long earlywood width and latewood width chronologies were developed from Chinese pine at two sites in the Hasi Mountains,north central China.The climate responses of these chronologies were analyzed and the LWW index used to derive sum-mer precipitation signals.Correlation analyses showed that LWW was particularly influenced by earlywood growth and recorded stronger climate signals of the previous year as EWW,rather than those of the current year with infrequent summer climate signals.However,after removing the effect of earlywood growth using a simple regression model,the adjusted LWW chronology(LWW_(adj))showed a strong relationship with July precipitation in dry years.This suggests that the LWW_(adj) chronology has the potential to be used to investigate long-term variability in summer precipitation in drought-limited regions.

Assessment of rehabilitation strategies for lakes affected by anthropogenic and climatic changes: A case study of the Urmia Lake, Iran

Over the last three decades,more than half of the world's large lakes and wetlands have experienced significant shrinkage,primarily due to climate change and extensive water consumption for agriculture and other human needs.The desiccation of lakes leads to severe environmental,economic,and social repercussions.Urmia Lake,located in northwestern Iran and representing a vital natural ecosystem,has experienced a volume reduction of over 90.0%.Our research evaluated diverse water management strategies within the Urmia Lake basin and prospects of inter-basin water transfers.This study focused on strategies to safeguard the environmental water rights of the Urmia Lake by utilizing the modeling and simulating(MODSIM)model.The model simulated changes in the lake's water volume under various scenarios.These included diverting water from incoming rivers,cutting agricultural water use by 40.0%,releasing dam water in non-agricultural seasons,treated wastewater utilization,and inter-basin transfers.Analytical hierarchy process(AHP)was utilized to analyze the simulation results.Expert opinions with AHP analysis,acted as a multi-criteria decision-making tool to evaluate the simulation and determine the optimal water supply source priority for the Urmia Lake.Our findings underscore the critical importance of reducing agricultural water consumption as the foremost step in preserving the lake.Following this,inter-basin water transfers are suggested,with a detailed consideration of the inherent challenges and limitations faced by the source watersheds.It is imperative to conduct assessments on the impacts of these transfers on the downstream users and the potential environmental risks,advocating for a diplomatic and cooperative approach with adjacent country.This study also aims to forecast the volumes of water that can be transferred under different climatic conditions—drought,normal,and wet years—to inform strategic water management planning for the Urmia Lake.According to our projection,implementing the strategic scenarios outlined could significantly augment the lake's level and volume,potentially by 3.57×109–9.38×109 m3 over the coming 10 a and 3.57×109–10.70×109 m3 in the subsequent 15 a.

Climatic and non-climatic factors driving the livelihood vulnerability of smallholder farmers in Ahafo Ano North District,Ghana

Smallholder farmers in Ahafo Ano North District,Ghana,face multiple climatic and non-climatic issues.This study assessed the factors contributing to the livelihood vulnerability of smallholder farmers in this district by household surveys with 200 respondents and focus group discussions(FGDs)with 10 respondents.The Mann–Kendall trend test was used to assess mean annual rainfall and temperature trends from 2002 to 2022.The relative importance index(RII)value was used to rank the climatic and non-climatic factors perceived by respondents.The socioeconomic characteristics affecting smallholder farmers’perceptions of climatic and non-climatic factors were evaluated by the binary logistic regression model.Results showed that mean annual rainfall decreased(P>0.05)but mean annual temperature significantly increased(P<0.05)from 2002 to 2022 in the district.The key climatic factors perceived by smallholder farmers were extreme heat or increasing temperature(RII=0.498),erratic rainfall(RII=0.485),and increased windstorms(RII=0.475).The critical non-climatic factors were high cost of farm inputs(RII=0.485),high cost of healthcare(RII=0.435),and poor condition of roads to farms(RII=0.415).Smallholder farmers’perceptions of climatic and non-climatic factors were significantly affected by their socioeconomic characteristics(P<0.05).This study concluded that these factors negatively impact the livelihoods and well-being of smallholder farmers and socioeconomic characteristics influence their perceptions of these factors.Therefore,to enhance the resilience of smallholder farmers to climate change,it is necessary to adopt a comprehensive and context-specific approach that accounts for climatic and non-climatic factors.

Physical Analysis of Atmospheric Phenomena Associated with Climatic Storms: Approach Study Related to Climate Change on Earth

Atmospheric phenomena are physical phenomena resulting from the correlation of atmospheric parameters of natural origin. They are associated with climatic storms and include lightning, thunder, global warming, wind, evaporation, rain, clouds, and snow. The formation and evolution of these phenomena remain complex according to their natural reference parameters. The numerical models defined in this study are equations based on models of atmospheric parameters. Applied in the atmosphere, they yield the equation of the key atmospheric phenomena. The distribution of these phenomena across the entire planet is the origin of the formation of climatic regions. Indeed, the constants obtained are 275.16 km/s for the speed of lightning, 3.99 GJ for the discharge energy of a thunderbolt, 276.15˚K for the temperature of global warming, 3.993 Km/h for the formation speed of winds and cyclones, 2.9963 Km/h for the speed of evaporation, 278.16˚K for the formation of rain, 274.1596˚K for the formation of clouds, and 274.1632˚K for snow formation. Moreover, this research conducts an analytical study approach to the phenomenon of climate change in the current era of industrialization, specifically analyzing the direct effects of global warming on atmospheric phenomena. Thus, with a temperature of 53.45˚C, global warming is considered maximal and will lead to very abundant rain and snow precipitations with maximum PW at 12.5 and 11.1 g/cm2 of water, surface water evaporation fluxes significantly above normal at a speed of 6.55 Km/h, increasingly violent winds at speeds far exceeding 5.43 Km/h, and catastrophic climatic effects. In summary, the aim of this research is to define the main natural phenomena associated with global climatic storms and to study the real impact of climate change on Earth.

Trade-Offs and Synergie Effects of Regulating Ecosystem Services along an Climatic Gradient in Slovakia

This study aims to assess the synergies and trade-offs of regulating ecosystem services. Ecosystem services are non-linearly interconnected and changes in one service can positively or negatively affect another. We evaluated ecosystem services based on biophysical indicators using an expert scoring system that determines the corresponding soil functions and is part of the existing databases available in Slovakia. This methodological combination enabled us to provide unique mapping and assessment of ecosystem services within Slovakia. Correlation analysis between individual regulating ecosystem services and climate regions, slope, texture, and altitude confirm the statistically significant influence of climate and slope in all agricultural land, arable land, and grassland ecosystems. Statistically significant synergistic effects were established between the regulation of the water regime and the regulation of soil erosion within each climate region, apart from the very warm climate region. Only in a very warm climate region was potential of regulating ecosystem services mutually beneficial for soil erosion control and soil cleaning potential (immobilization of inorganic pollutants).

Numerical Models and Methods of Atmospheric Parameters Originating in the Formation of the Earth’s Climatic Cycle

Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in2/s for humidity, 2.993 in2/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .

Climatic drivers of the Canadian wildfire episode in 2023

2023年5月加拿大发生极端野火事件并持续4个月时间.野火污染物排放对当地和下风向的空气质量造成了严重影响.本文对驱动此次野火事件的气象因子和相关大尺度环流进行了探究.研究表明,此次极端野火排放的二氧化碳相较往年同期增长了0.37Gt (527.1%).受罗斯贝波频散影响,同期加拿大中西部区域出现持续的位势高度正异常,促进气流下沉并引发局地高温,中西部区域5-6月最高温平均上升8.11℃,部分区域甚至超过10℃,导致野火排放的显著增长.受有利地形和泛北极地区快速增暖的影响,预期未来加拿大中西部发生极端野火的概率可能会显著上升.

NUMERICAL STUDY OF THE SHORT-TERM CLIMATIC OSCILLATION FORCED BY EL NINO DURING NORTHERN WINTER

Using a nine-layer global spectral model, numerical schemes with two different SST distributions in January (control case and abnormal case) have been tested to study the climatic effect, propagation charateris- tics and the maintenance mechanism of the short-term climatic oscillation caused by El Nino during northern winter. The main results are as follows: (1) During northern winter, there exist two wave trains because of the influence of El Nino. One is similar to PNA pattern, and the other is similar to EUP pattern. (2) The PNA-like wave train caused by the anomalous SST forcing in central and eastern equatorial Pacific Ocean is due to the response of ultralong wave and long wave components of Rossby mode, and the EUP-like wave train crossing Eurasia is mainly due to the wave component of Rossby mode. (3) During northern winter, the warm water region in central equatorial Pacific Ocean is the source of forced wave trains. (4) In northern winter, the energy source for maintaining the short-term climatic oscillation is from the interaction between eddies, and between eddy and zonal flow.

FORMATION MECHANISM AND PROPAGATION CHARACTERISTICS OF THE EQUATORIAL THERMALLY-FORCED SHORT-TERM CLIMATIC OSCILLATION DURING THE NORTHERN SUMMER

In this study, in order to investigate the global climatic oscillations forced by sea surface temperature (SST) anomalies over equatorial central-eastern Pacific, two numerical schemes with different SST distri-butions (normal and anomalous cases) are tested by using a nine-layer global spectral model. Experiment results show that (i) in northern summer, a wave train that is similar to the teleconnection pattern suggested by Nitta (1987) and Huang (1987) in the Northern Hemisphere and another one in the Southern Hemisphere are reproduced; (ii) simulated results suggest that the response of atmosphere in middle-high latitudes of both hemispheres to an anomalous heating source is more sensitive in tropical western Pacific than in equatorial central-eastern Pacific; and (iii) in northern summer, the formation of low-frequency oscillations on monthly (seasonal) time scale results from energy dispersion as well as interactions between eddies and zonal flow; and between eddies.

Influence of Soil Moisture and Vegetation on Climate (Ⅱ)——Numerical Experiments on Persistence of Short-term Climatic Anomalies

Numerical experiments on the influence of soil moisture and vegetation on the persistence of short-term climatic anomalies are performed with a vertical one-dimensional climate model in the atmosphere-soil-vegetation system.The experimental results indicate that, as shown by theoretical analysis in Part Ⅰ of this paper,soil hydrology is crucially important for maintenance of short-term climatic anomalies over land areas. Possible physical process concerned with impact of soil moisture and vegetation on the persistence is further discussed.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Agro-Climatic Risks Analysis in Climate Variability Context in Ségou Region

In the Sahel region, the population depends largely on rain-fed agriculture. In West Africa in particular, climate models turn out to be unable to capture some basic features of present-day climate variability. This study proposes a contribution to the analysis of the evolution of agro-climatic risks in the context of climate variability. Some statistical tests are used on the main variables of the rainy season to determine the trends and the variabilities described by the data series. Thus, the paper provides a statistical modeling of the different agro-climatic risks while the seasonal variability of agro-climatic parameters was analyzed as well as their inter annual variability. The study identifies the probability distributions of agroclimatic risks and the characterization of the rainy season was clarified.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Call for Papers:Special Issue“Past and Present Climatic Change in Antarctica:Geological Proxies and Biological Processes”

Dear colleagues,We would like to invite you to participate in the special issue of the journal Advances in Polar Science(ISSN 1674-9928)entitled“Past and Present Climatic Change in Antarctica:Geological Proxies and Biological Processes”,which is expected to be published in March 2024 as general issue(Vol.35,No.1).We are honored to invite Dra.Carolina Acosta Hospitaleche,Dr.Javier N.Gelfo,Dr.Marcelo Reguero,Dra.Adolfina Savoretti and Dra.

Climate-growth relationships of Pinus tabuliformis along an altitudinal gradient on Baiyunshan Mountain,Central China

A set of standard chronologies for tree-ring width(TRW),earlywood width(EWW)and latewood width(LWW)in Pinus tabuliformis Carr.along an altitudi-nal gradient(1450,1400,and 1350 m a.s.l.)on Baiyunshan Mountain,Central China to analyze the effect of varying temperature and precipitation on growth along the gradi-ent.Correlation analyses showed that at all three altitudes and the TRW and EWW chronologies generally had signifi-cant negative correlations with mean and maximum tem-peratures in the current April and May and with minimum temperatures in the prior July and August,but significant positive correlations with precipitation in the current May.Correlations were generally significantly negative between LWW chronologies and all temperatures in the prior July and August,indicating that the prior summer temperature had a strong lag effect on the growth of P.tabuliformis that increased with altitude.The correlation with the standard-ized precipitation evapotranspiration index(SPEI)confirmed that wet conditions in the current May promoted growth of TR and EW at all altitudes.Significant altitudinal differences were also found;at 1400 m,there were significant positive correlations between EWW chronologies and SPEI in the current April and significant negative correlations between LWW chronologies and SPEI in the current September,but these correlations were not significant at 1450 m.At 1350 m,there were also significant negative correlations between the TRW and the EWW chronologies and SPEI in the prior October and the current July and between LWW chronology and SPEI in the current August,but these cor-relations were not significant at 1400 m.Moving correlation results showed a stable response of EWW in relation to the SPEI in the current May at all three altitudes and of LWW to maximum temperature in the prior July-August at 1400 m from 2002 to 2018.The EWW chronology at 1400 m and the LWW chronology at 1450 m were identified as more suitable for climate reconstruction.These results provide a strong scientific basis for forest management decisions and climate reconstructions in Central China.

Climatic Indices’ Analysis on Extreme Precipitation for Tanzania Synoptic Stations

Like other countries in East Africa, Tanzania has been affected by extreme precipitation incidences both socially and economically. Determining the trend and variability features of extreme precipitation in the country is crucial. This study used data from 28 meteorological stations for 1981-2020 period to give an annual and seasonal analysis of the patterns of 10 ETCCDI’s extreme precipitation indices over the regions. At annual scale, the results showed that increasing trends had high frequency percentage than the decreasing ones, collecting about 76% in total. The decreasing trend was approximately 24%, and most of the stations with increasing percentage in trend are concentrated in Northern coast, Central, West, North-eastern highlands and Lake Victoria Basin. Most of the stations depicted negative trend are concentrated over Southern region. This highlights that extreme precipitation events have increased over the country for the period 1981-2020. At seasonal scale, during October to December (OND);the patterns of extreme precipitation climatic indices except R99p, showed positive significant increasing trend over Lake Victoria Basin and some Western parts of the country. In general, spatial patterns indicate decrease of precipitation over most parts of the country during OND. The seasonal average time series depicted non-significant positive trend during March to April (MAM) season, except for Consecutive Wet Days (CWD) which showed non-significant decreasing trend. Over the highest mountain in Africa, Kilimanjaro;the study has revealed significant decrease in Annual total-wet Precipitation (PRCPTOT), the number of heavy (very heavy) days of precipitation R10 mm (R20 mm) and Consecutive Wet Days (CWD) during MAM season. While the maximum one-day precipitation amount (RX1 day) was observed to decrease significantly over the Mountain during OND season. The result is very important in risk assessment and preparedness perspective in planning climate change mitigation and adaptations for different sectors like Tourism, Agriculture, Water and Energy.