Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Light Use Efficiency Model Based on Chlorophyll Content Better Captures Seasonal Gross Primary Production Dynamics of Deciduous Broadleaf Forests

Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.

Modeling the Seasonal Temperature from Hourly Data: Seasonal Divisions

In this work, we present a general theoretical study leading to analytical expression of the seasonal temperature at the near surface that is expected to evaluate any area seasonal temperature of the world using the least square method to fit the hourly data to the theoretical curve of the temperature. It is shown that the temperature is globally the result of two contributions: the contribution of the revolution movement of the terrestrial globe on its elliptical orbit around the sun, the contribution of the spin-orbit coupling for the rotation movement of the terrestrial globe around its polar axis and its revolution movement. The orbital behavior of the temperature is used to find the seasonal divisions of the climate for the local area considered. The whole expression of the temperature is very useful for the meteorological needs. The contribution of the human activities and natural instabilities are the results of discrepancies which increase errors (standard deviations).

目的论视角下文化负载词英译研究——以纪录片Seasons of China为例

在当今世界文化交流愈发频繁的背景下,推动中华文明走向世界成为重中之重。翻译作为推动跨文化交流的重要手段之一,是连接不同文化的桥梁。而文化负载词由于其涵盖的大量民族特色词汇,成为翻译过程中的挑战,同时也是文化传播的重要工具。文章基于目的论视角,以纪录片Seasons of China中的中文文化负载词英译为例,探讨译者在翻译过程中采取的恰当翻译方法,以期为纪录片的文化负载词翻译研究提供一定的借鉴与参考。

Seasonal changes in Hermit Thrush (Catharus guttatus) song syntax

In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.

Degree of shade tolerance shapes seasonality of chlorophyll, nitrogen and phosphorus levels of trees and herbs in a temperate deciduous forest

Forest productivity is closely linked to seasonal variations and vertical differentiation in leaf traits.However,leaf structural and chemical traits variation among co-existing species,and plant functional types within the canopy are poorly quantified.In this study,the seasonality of leaf chlorophyll,nitrogen(N),and phosphorus(P)were quantified vertically along the canopy of four major tree species and two types of herbs in a temperate deciduous forest.The role of shade tolerance in shaping the seasonal variation and vertical differentiation was examined.During the entire season,chlorophyll content showed a distinct asymmetric unimodal pattern for all species,with greater chlorophyll levels in autumn than in spring,and the timing of peak chlorophyll per leaf area gradually decreased as shade tolerance increased.Chlorophyll a:b ratios gradually decreased with increasing shade tolerance.Leaf N and P contents sharply declined during leaf expansion,remained steady in the mature stage and decreased again during leaf senescence.Over the seasons,the lower canopy layer had significantly higher chlorophyll per leaf mass but not chlorophyll per leaf area than the upper canopy layer regardless of degree of shade tolerance.However,N and P per leaf area of intermediate shade-tolerant and fully shade-tolerant tree species were significantly higher in the upper canopy than in the lower.Seasonal variations in N:P ratios suggest changes in N or P limitation.These findings indicate that shade tolerance is a key feature shaping inter-specific differences in leaf chlorophyll,N,and P contents as well as their seasonality in temperate deciduous forests,which have significant implications for modeling leaf photosynthesis and ecosystem production.

Intra-tooth stable isotope analysis reveals seasonal dietary variability and niche partitioning among bushpigs/red river hogs and warthogs

How animals respond to seasonal resource availability has profound implications for their dietary flexibility and realized ecological niches.We sought to understand seasonal dietary niche partitioning in extant African suids using intra-tooth stable isotope analysis of enamel.We collected enamel samples from canines of red river hogs/bushpigs(Potamochoerus spp.)and third molars of warthogs(Phacochoerus spp.)in 3 different regions of central and eastern Africa.We analyzed multiple samples from each tooth and used variations in stable carbon and oxygen isotope ratios(δ^(13)C andδ^(18)O)and covariances between them to infer seasonal dietary changes.We found that most Phacochoerus display C_(4)-dominated diets,while most Potamochoerus display C_(3)-dominated diets.Phacochoerus and Potamochoerus that co-occur in the same region display no overlap in intra-toothδ^(13)C,which suggests dietary niche partitioning.They also show divergingδ^(13)C values as the dry seasons progress and convergingδ^(13)C values during the peak of the rainy seasons,which suggests a greater dietary niche separation during the dry seasons when resources are scarce than during the rainy season.We found statistically significant cross-correlations between intra-toothδ^(13)C andδ^(18)O in most specimens.We also observed a temporal lag betweenδ^(13)C andδ^(18)O in some specimens.This study demonstrates that intra-tooth stable isotope analysis is a promising approach to investigate seasonal dietary niche variation.However,large inter-individual variations inδ^(18)O at certain localities can be challenging to interpret.Future studies that expand the intra-tooth stable isotope surveys or include controlled feeding experiments will improve its application in ecological studies.

Does season regulate heterochronous leaf growth?Mechanisms of petiole-lamina trade-offs in broad-leaved woody plants of the Tianshan Mountains

As the core of leaf functional traits,the trade-off relationship between the petiole and lamina expresses the plant's adaptability to the environment in terms of support structure and photosynthesis.We investigated the proportions of allometric growth in the relationship between the petiole and the lamina of broadleaf woody plants in temperate highland Tianshan Mountains montane forests through three dimensions(length,area,and mass),including the length of the lamina(LL)and the length of the petiole(PL),and the area of the lamina(LA)and petiole cross sectional area(PCA)versus the mass of the lamina(LM)and the mass of the petiole(PM),as well as exploring the characteristics of the variance in response to seasonal changes.We found that the functional traits in all three dimensions showed a clear convergent evolution as the seasons progressed,that is,a“seasonal effect”of increasing and then decreasing.The effect of the petioleelamina relationship under spring was minimal in the area dimension;the effects of the threeedimensional relationships of the traits were all highest in summer,and the effect of the petioleelamina relationship was lower in autumn.We also found that petiole traits are simultaneously and multiply affected by lamina traits,with LA and LM having additional effects on the length/mass and area dimensions,respectively.Compared to tree species,shrub species significantly require more light intensity and support capacity.Compound-leaved plants would invest more in photoluminescence,increasing leaf light capture efficiency and static load and dynamic resistance.Our results suggest that plants have rather complex trade-off mechanisms at the leaf level influencing their ability to adapt to the environment,emphasize the need for leaf-level studies on the relationships between functional traits in plants,and illustrate the importance of the season as a distinct time scale for plant trade-off mechanisms.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

Oriented Generation of Novel Thermo-Sensitive Genic Male Sterile Lines with Improved Grain Shape and Outcrossing Rate in Early-Season Rice

Two-line hybrid rice with excellent quality is preferred in the Chinese market.However,there is a trade-off between reducing costs for hybrid seed production and lowering the outcrossing rate of the sterile line,which is largely determined by the stigma exsertion rate(SER).In this study,we constructed mutants of male sterility lines with improved grain length(GL)and SER in three elite early-season indica rice varieties through targeted manipulation of the TMS5 and GS3 genes using CRISPR/Cas9-mediated multiplex systems.We obtained a series of marker-free gs3 single mutants and gs3tms5 double mutants with significantly higher SER,longer grains,and increased 1000-grain weight compared with the wild type(WT).Importantly,the typically thermo-sensitive genic male sterile(TGMS)trait with a higher SER was observed in gs3tms5 mutants,and their F1 hybrids exhibited remarkable improvements in grain shape and yield-related traits.Our findings provided an efficient method to generate new valuable TGMS germplasm with improved SER through the mutagenesis of GS3 and TMS5 synergistically,and demonstrated that GS3 had pleiotropic effects on grain size,SER,and grain quality in early-season indica rice.

Seedling dynamics differ between canopy species and understory species in a tropical seasonal rainforest,SW China

We used 11 years of census data from 450 seedling quadrats established in a 20-ha forest dynamics plot to study seedling dynamics in tree species of a tropical seasonal rainforest in Xishuangbanna,southwestern China.We found that overall seedling recruitment rate and relative growth rate were higher in the rainy season than in the dry season.Both the recruitment rate of seedlings from canopy tree species(two species)and the relative growth rate of seedlings from understory species(nine species)were higher in the rainy season than in the dry season.However,in the rainy season,the recruitment rate of seedlings was higher for canopy tree species than for understory tree species.In addition,relative growth rate of seedlings was higher in the canopy species than in understory seedlings in the dry season.We also observed that,in both rainy and dry seasons,mortality rate of seedlings was higher for canopy species than for understory species.Overall,canopy tree species appear to have evolved a flexible strategy to adapt to the seasonal changes of a monsoon climate.In contrast,understory tree species seem to have adopted a conservative strategy.Specifically,these species mainly release seedlings in the rainy season and maintain relatively stable populations with a lower mortality rate and recruitment rate in both dry and rainy seasons.Our study suggests that canopy and understory seedling populations growing in forest understory may respond to future climate change scenarios with distinct regeneration strategies.

Litter production and leaf nutrient concentration and remobilization in response to climate seasonality in the central Amazon

Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seasonality remain largely unknown for the central Amazon.This study meas-ured litterfall production,leaf nutrient remobilization,and leaf area index on a forest plateau in the central Amazon.Litterfall was measured at monthly intervals during 2014,while nitrogen,phosphorus,potassium,calcium and mag-nesium concentrations of leaf litter and canopy leaves were measured in the dry and rainy seasons,and remobilization rates determined.Leaf area index was also recorded in the dry and rainy seasons.Monthly litterfall varied from 33.2(in the rainy season)to 87.6 g m^(-2) in the dry season,while leaf area index increased slightly in the rainy season.Climatic seasonality had no effect on concentrations of nitrogen,calcium,and magnesium,whereas phosphorous and potassium responded to rainfall seasonality oppositely.While phosphorous increased,potassium decreased during the dry season.Over seasons,nitrogen,potassium,and phosphorous decreased in leaf litter;calcium increased in leaf litter,while magnesium remained unaffected with leaf aging.Regardless,the five nutrients had similar remobilization rates over the year.The absence of climate seasonality on nutrient remobilization suggests that the current length of the dry season does not alter nutrient remobilization rates but this may change as dry periods become more prolonged in the future due to climate change.

Seasonal variation of mesoscale eddy intensity in the global ocean

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

Seasonal Variation of Hearing Sensitivity in the Red-eared Slider(Trachemys scripta elegans)

Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.

THE THRESHOLD DYNAMICS OF A WATERBORNE PATHOGEN MODEL WITH SEASONALITY IN A POLLUTED ENVIRONMENT

This paper concentrates on the dynamics of a waterborne pathogen periodic PDE model with environmental pollution.For this model,we derive the basic reproduction number R0and establish a threshold type result on its global dynamics in terms of R0,which predicts the extinction or persistence of diseases.More precisely,the disease-free steady state is globally attractive if R_(0)<1,while the system admits at least one positive periodic solution and the disease is uniformly persistent if R_(0)>1.Moreover,we carry out some numerical simulations to illustrate the long-term behaviors of solutions and explore the influence of environmental pollution and seasonality on the spread of waterborne diseases.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Active case detection using bi-valent malaria rapid diagnostic test in non-transmission season impacts upcoming malaria transmission

Despite a considerable global effort to eradicate malaria in the last few years,the disease burden in 2020 was 241 million,with 627000 deaths.India accounted for 83%of cases reported in the World Health Organization(WHO)South-East Asia Region(WHO malaria report 2021).Nevertheless,India has shown its commitment to eliminating malaria from the country by framing the National Strategic Plan(NSP),according to which the districts have been stratified into four categories based on the malaria transmission intensity and the indicator used for categorization of the districts was annual parasite incidence(API).In North-East India,many highly endemic districts(API>2)are in the“Intensified Control Phase”and fall under category栿[1].A few pockets in such districts have disproportionate(API>10)due to forest and tribal dominance in those regions.The previous reports demonstrated a high malaria burden(API>10)in the district East Garo Hills[2];however,a few pockets of the district Udalguri are malaria hotspots with the API>5[3].

Seasonal Variation of the Sea Surface Temperature Growth Rate of ENSO

El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature(SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Ni?o-3.4 region(5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection(surface heat flux) is positive(negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.

Changes in Leaf Stomatal Properties in Rice with the Growing Season

Transplanting rice varieties grown in different seasons can lead to different yields due to different dry matterproduction. Early-season rice varieties transplanted in the late season can obtain high yields with short-growthduration and higher yields driven by higher dry matter production. To make clear the variations in dry matterproduction across seasons, four early-season rice varieties were chosen for late-season transplantation. The grainyield, dry matter accumulation, leaf photosynthetic, and leaf stomatal properties were studied. It was observedthat the average yields of these four varieties in the late season were 33% greater, despite a reduced growth periodof 13 days in comparison with the early season. Furthermore, there was a notable increase in both total and postheadingdry matter production during the late season. The leaf net photosynthetic rate, stomatal area, stomatalwidth, and stomatal length were higher in the late season. Despite no significant difference in stomatal densitybetween seasons, strong positive linear relationships were observed between net photosynthetic rate and stomatalconductance, and between stomatal conductance and area. These relationships demonstrate that the increase ofthe stomatal width and length of the leaves in the late season leads to an increase in the stomatal area, therebyincreasing the stomatal conductance and enhancing the photosynthesis of the leaves. Consequently, this leads togreater dry matter production and a higher yield compared to the early season. Therefore, when breeding newhigh-yielding and short-growing varieties, the large stomatal area can be used as a reference index.