Temporal Correlation Coefficient Research Articles

Modeling and Analysis of Long-Term Average User Throughput in Mobile Ultra Dense Networks

In the existing literatures, the joint coverage probability is used to analyze the dependence in coverage probability over different time slots. However, these researches concentrate on the correlation caused by the stability of channels, and ignored the effect of the fluctuation caused by randomness from mobility and small-scale fading. In this letter, the exact expression of long-term average throughput is derived based on a novel analytical framework, which simultaneously calculate the average transmission success probability considering correlation and fluctuation descended from the joint coverage probability. Specifically, the fluctuation in network performance is characterized by incorporating the distance variation and signal attenuation of mobile users into the statistics of channel gains. In addition, the temporal correlation coefficient of the received signal is measured under the mobility model. Simulations are also conducted with the analysis results, which shows that the temporal correlation increases with increasing path-loss factor, the long-term average user throughput decreases with increasing mobility model factor.

Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

Optimized Algorithm for Muscular Diseases Recognition Based On Temporal Parameters Analysis and Correlation Coefficients

In this paper, an optimized algorithm based on temporal parameters analysis and correlation coefficients is presented in order to perform muscular diseases recognition. Statistical information was measured of three classes signals (Healthy, Myopathy and Neuropathy conditions). The temporal parameters that were initially proposed (14) were optimized based on the correlation coefficients. Thus, only 9 parameters were selected for optimized the algorithm, and the time required for training and recognition is ≈ 0.2s and ≈ 4ms, respectively.

Global Modeling of Secondary Organic Aerosol With Organic Nucleation

AbstractOrganic nucleation has been identified as an important way to form secondary organic aerosol (SOA) and change the number concentration of aerosol and thus its climate effect. A global atmospheric chemistry model is developed to include a comprehensive organic nucleation scheme that includes heteromolecular nucleation of sulfuric acid and organics, neutral pure organic nucleation, and ion‐induced pure organic nucleation. Our model simulation shows reasonable agreement with the seasonal as well as spatial pattern of organic carbon concentration in America, while it fails to predict the seasonal pattern of organic carbon in Europe due to the lack of sharp increases in primary organic aerosol emissions in the winter. Including organic nucleation decreases the bias of the annual average particle number concentration at 54% of the available observation sites and increases the temporal correlation coefficients at 58% of the sites. Ion‐induced pure organic nucleation contributes the most to the total organic nucleation rate, which peaks around 400 hPa in the tropics. Heteromolecular nucleation of sulfuric acid and organics dominates the total organic nucleation rate in the summer and mostly occurs in the lower troposphere. The number concentration of particles formed from organic nucleation (newSOA) in the nucleation and Aitken modes is highest in the tropics, while accumulation mode newSOA is highest in the Northern Hemisphere due to growth as a result of the condensation of sulfate. Three sensitivity experiments suggest that more studies are needed to investigate the formation mechanism of newSOA, so that a more accurate simulation of the spatial and size distribution of newSOA can be developed.

On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils

For soils with shallow groundwater and high organic carbon content, water table depth (WTD) is a key parameter to describe their hydrologic state and to estimate greenhouse gas emissions (GHG). Since the microwave backscatter coefficient (σ0) is sensitive to soil moisture, the application of Sentinel-1 satellite data might support the monitoring of these climate-relevant soils at high spatial resolution (~100 m) by detecting spatial and temporal changes in local field and water management. Despite the low penetration depth of the C-band, σ0 is influenced by shallow WTD fluctuations via the soil hydraulic connection between the water table and surface soil. Here, we analyzed σ0 at 60 monitoring wells in a drained temperate peatland with degraded organic soils used as extensive grassland. We evaluated temporal Spearman correlation coefficients between σ0 and WTD considering the soil and vegetation information. To account for the effects of seasonal vegetation changes, we used the cross-over (incidence) angle method. Climatologies of the slope of the incidence angle dependency derived from two years of Sentinel-1 data and their application to the cross-over angle method did improve correlations, though the effect was minor. Overall, averaged over all sites, a temporal Spearman correlation coefficient of 0.45 (±0.17) was obtained. The loss of correlation during summer (higher vegetation, deeper WTD) and the effects of cuts and grazing are discussed. The site-specific general wetness level, described by the mean WTD of each site was shown to be a major factor controlling the strength of the correlation. Mean WTD deeper than about −0.60 m lowered the correlations across sites, which might indicate an important limit of the application.

PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model.

Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland-specific land surface hydrology module (PEAT-CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland-specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT-CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT-CLSM are based on literature data. A suite of CLSM and PEAT-CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM’s simulated groundwater tables are too deep and variable, whereas PEAT-CLSM simulates a mean groundwater table depth of −0.20 m (snow-free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT-CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements.

Performance analysis of receive diversity under time-varying and spatially correlated channels using partial CSI

In this paper, a single input multiple output system is considered with L receive antennas and the underlying channels are assumed to be time varying with temporal correlation coefficient a and spatially correlated with correlation coefficient $$\rho $$ . Further, the channel is assumed to be identically distributed using Rayleigh fading channels and characterized by first order autoregressive model. For the detection, we assume partial channel state information (CSI) available at the receiver. The partial CSI is in the form of a known preamble (one symbol) only before beginning of a frame of size N symbols. Further, the preamble is imperfectly known with a variance of error $$\sigma _e^2$$ . For the assumed system, closed form expressions of symbol error rate (SER) are derived for M-PSK and M-QAM constellations under the compound effect of spatially correlated channels, temporally correlated channels and partial CSI at the Receiver. The derived expressions are functions of average SNR per symbol, $$\rho $$ , a, $$\sigma _e^2$$ , N, L and modulation order M. Further, these expressions are reduced for some special cases and compared with prevailing results in literature. The analytical expressions are also validated by comparing them with the corresponding simulation results. The derived expressions are very useful to select N, L and M to overcome the deterioration in SER due to adverse effects of $$\rho $$ , a and $$\sigma _e^2$$ .

Development of a rice tungro epidemiological model for seasonal disease risk management in the Philippines

Through an integration of seasonal climate forecasts (SCF) and rice disease epidemiological models, a potential risk for rice disease epidemics can be predicted even before a cropping season starts, allowing agricultural stakeholders to proactively manage risk on a seasonal scale. The objective of this study is to develop and evaluate an epidemiological “rtdSim” model for tungro, a vector-born virus disease of rice, aiming at producing a seasonal tungro risk prediction for the Bicol Region of the Philippines. Given the complex nature of disease pyramid (virus, vector, host, and environment) and their interactions with each other, a simplified model capable of predicting tungro epidemics makes use of assumption-based, simplified model algorithms. The rtdSim calculates the rate of rice hill infection with the rice tungro virus through its vector, the green leafhopper (GLH), using two linked modules: the “GLH population module” simulating complete GLH lifecycle population dynamics and immigration from nearby rice paddies; and the “Tungro virus infection module” simulating virus transmissions between infected rice hills and the GLH vector. The rtdSim is successfully calibrated and validated only for wet season, resulting in a reasonably high level of agreement: a good graphical fit for the incidence level of the disease at 60 days after transplanting and a temporal correlation coefficient of 0.70 between the observed and the simulated epidemics in the Bicol Region. The rtdSim simulations are sensitive to seasonal variations of temperature and rainfall, indicating the potential applicability of the model for seasonal RTD prediction with SCF. The present study highlights the potential for developing a SCF-based early warning system for rice diseases, enabling better decision-making for prevention of potential disease outbreaks on a seasonal scale with integrated pest management (IPM) strategies, e.g. switching to resistant varieties and using synchronous planting, rotations, or fallow periods to avoid peak risk pressure.

Potential predictability of Arabian peninsula summer surface air temperature in the North American multimodel ensemble

The present study evaluates the potential predictability, and prediction skill of surface air temperature (SAT) over the Arabian peninsula (AP) referred to as AP-SAT hereafter, during boreal summer (June–July–August: JJA) from 1982 through 2017. The study was made by considering the single model, and a multimodel ensemble (MME) approach. The seasonal prediction data for JJA SAT initialized at May (Lead-1), and April (Lead-2) observed initial conditions, from six coupled atmosphere–ocean global circulation models included in the North American Multimodel Ensemble, is utilized. The potential predictability (PP) is estimated through the estimation of the signal-to-noise ratio (S/N ratio) and perfect model correlation (PMC), while prediction skill is computed by the temporal anomaly correlation coefficient (TCC). All models show a decrease in potential predictability and prediction skill with an increase in lead time. The CFSv2 and NASA models show higher PP, which indicates a high potential predictive skill for summer AP-SAT in these models. However, both models show quite low values of TCC all over the AP domain, which is an indication of overconfident predictions. The three geophysical fluid dynamics laboratory models show high prediction skill at both leads. An essential finding of the predictive analysis (PMC and TCC) is that the MME does outperform the individual model at both leads for summer AP-SAT. Each model captures the observed relationship between spatially averaged AP-SAT with sea surface temperature (SST) and 200 hPa geopotential height (Z200) during JJA, with varying details. Persistent model biases impact negatively model predictability and skill, and better AP-SAT and SST teleconnection pattern in models lead to higher predictability. Improvements in initial conditions, model physics, and larger ensemble size are necessary elements to enhance the summer AP-SAT potential predictability and skill.

Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model

In this study, the performance of Beijing Climate Center's Climate System Model (BCC_CSM) is analysed in terms of China summer rainfall prediction, especially its capability to reproduce the spatial structure and temporal variation of the leading modes of empirical orthogonal function decomposition. Results show that the BCC_CSM has predictability for the summer mean rainfall pattern, namely, reproducing more rainfall in South China and maritime area, and less rainfall in North China and inland area. The model can reproduce the spatial pattern of the first two leading modes of observed summer rainfall; however, it fails to show correct interannual variability of the first two principle components, which may lead to a low skill in summer rainfall anomaly prediction over most parts of China. The leading mode‐based correction (LMC) method is then applied for the post processing procedure to correct prediction errors. Cross validation confirms that the LMC method can integrate the historical spatial pattern information of observed summer rainfall with the original model prediction, making the corrected model output better reflect spatial structure of the leading modes and their interannual variation. Compared with the original model output, the positive temporal correlation coefficient area for the corrected China summer rainfall anomalies is thus increased, and the average spatial correlation coefficient during 1991–2015 is improved from −0.01 before the correction to 0.17. The LMC method shows its potential for improving the prediction skill of BCC_CSM, which can be used in operational prediction.

Exploring the influence of two inventories on simulated air pollutants during winter over the Yangtze River Delta

Currently, haze events in winter occur more frequently than decades ago, especially in Eastern and Central China, including the Yangtze River Delta (YRD). WRF-Chem is applied in this study to explore the discrepancies of the simulated air pollutants induced by employing different emission inventories, particularly during haze events. Two inventories are involved in this study, MEIC (Multi-resolution Emission Inventory for China) and GlobEmission (inventory from GlobEmission project emission estimates), representing the emission inventories based on quite different ways. We first compared monthly emissions of SO2 and NOx in two inventories during January over YRD, and found the mean differences of SO2 (NOx) are 20% (7%), with the ranges of 0%–47% (−100%–100%). The largest differences are both found in Shanghai, with 47% for SO2 (MEIC in 2010, GlobEmission in 2014), and 45% for NOx (MEIC in 2010, GlobEmission in 2015), respectively, partly because the reduction of emissions was large during the 12th Five-Year Plan (2011–2015) in this area. By comparing the simulated air pollutants mass concentrations using two inventories with in-situ observations during January 2015, we found that the simulated SO2 using MEIC and GlobEmission are both higher than observations, with mean normalized biases of 207% and 121% over YRD, respectively, and much larger in the city cluster of Nanjing-Shanghai, where are nearly four (MEIC) and three times (GlobEmission) higher than observations. In contrast, NO2 simulations using GlobEmission are lower than observations (22%) and MEIC simulations (45%) over YRD. The largest biases of GlobEmission simulation are found in Zhejiang province (over 70%). The biases of simulated monthly mean PM2.5 are 38% (MEIC) and 30% (GlobEmission) over YRD, respectively. A case study during heavy haze event shows that the biases increase to 61% (MEIC) and 39% (GlobEmission), and spatial correlation coefficient in the simulation using GlobEmission increase to 0.69 over YRD. Temporal correlation coefficients in the city cluster of Nanjing-Shanghai increase from 0.5 (MEIC) to 0.7 (GlobEmission).

Multi-model seasonal forecasts for the wind energy sector

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.

A new statistical correction strategy to improve long‐term dynamical prediction

In this study, a new statistical strategy to improve the long‐term prediction skill of a numerical model was developed. This new strategy begins by finding the major principal time series (PTs) in the observations using the self‐organizing map (SOM) method. Next, values at the model grid points that are highly correlated with the observational PTs for each ensemble member (EM) are combined to yield a modelled PT. Finally, the model prediction is corrected using the model PTs from the previous step. As the predictors for correction are objectively selected from among the signals found in model prediction, automatically considering their statistical correlation with predictands, the correction strategy is relatively free from the problem of selecting the proper predictor compared to conventional statistical correction methods. In addition, SOM shows a better performance in classifying nonlinear complex patterns than conventional data analysis methods, while both SOM and conventional methods such as the empirical orthogonal function show a comparable performance when classifying linear patterns. The new strategy is applied to the 12‐month‐lead sea surface temperatures hindcasted by the Pusan National University coupled general circulation model. After correction using the new strategy, temporal correlation coefficients and the hit rate are increased while normalized root mean square errors and the false alarm rate are decreased for each season and each lead time. The correction becomes more effective as the lead time increases. In particular, this correction effect is large over the region where the prediction skill without correction is apparently low, which implies that the biases leading to poor prediction skills are effectively reduced by the new strategy. Additionally, the prediction skill is steadily improved for all lead times as the number of EMs is increased, whereas it reaches a plateau when the number of neurons in the output layer of the SOM method exceeds a certain threshold.

A Dissociation in Effects of Risperidone Monotherapy on Functional and Anatomical Connectivity Within the Default Mode Network.

Respective changes in functional and anatomical connectivities of default mode network (DMN) after antipsychotic treatment have been reported. However, alterations in structure-function coupling after treatment remain unknown. We performed diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging in 42 drug-naive first-episode schizophrenia patients (FESP) both at baseline and after 8-weeks risperidone monotherapy, and in 38 healthy volunteers. Independent component analysis was used to assess voxel-wise DMN synchrony. A 3-step procedure was used to trace fiber paths between DMN components. Structure-function couplings were assessed by Pearson's correlations between mean fractional anisotropy and temporal correlation coefficients in major tracts of DMN. Pretreatment, FESP showed impaired functional connectivity in posterior cingulate cortex/precuneus (PCC/PCUN) and medial prefrontal cortex (mPFC), but no abnormalities in fibers connecting DMN components. After treatment, there were significant increases in functional connectivities of PCC/PCUN. Increases in functional connectivity between PCC/PCUN and mPFC correlated with improvement in positive symptoms. The structure-function coupling in tracts connecting PCC/PCUN and bilateral medial temporal lobes decreased after treatment. No alterations in DMN fiber integrity were detected. This combination of functional and anatomical findings in FESP contributes novel evidence related to neurobehavioral treatment effects. Increased functional connectivities between PCC/PCUN and mPFC may be treatment response biomarkers for positive symptoms. Increases in functional connectivities, no alterations in fiber integrity, combined with decreases in structural-functional coupling, suggest that DMN connectivities may be dissociated by modality after 8-week treatment. Major limitations of this study, however, include lack of repeat scans in healthy volunteers and control group of patients taking placebo or comparator antipsychotics.

Towards Estimating Land Evaporation at Field Scales Using GLEAM

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.

A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability

The Asia Pacific Economic Cooperation (APEC) Climate Center (APCC) in-house model (Seamless Coupled Prediction System: SCoPS) has been newly developed for operational seasonal forecasting. SCoPS has generated ensemble retrospective forecasts for the period 1982–2013 and real-time forecasts for the period 2014–current. In this study, the seasonal prediction skill of the SCoPS hindcast ensemble was validated compared to those of the previous operation model (APEC Climate Center Community Climate System Model version 3: APCC CCSM3). This study validated the spatial and temporal prediction skills of hindcast climatology, large-scale features, and the seasonal climate variability from both systems. A special focus was the fidelity of the systems to reproduce and forecast phenomena that are closely related to the East Asian monsoon system. Overall, both CCSM3 and SCoPS exhibit realistic representations of the basic climate, although systematic biases are found for surface temperature and precipitation. The averaged temporal anomaly correlation coefficient for sea surface temperature, 2-m temperature, and precipitation from SCoPS is higher than those from CCSM3. Notably, SCoPS well captures the northward migrated rainband related to the East Asian summer monsoon. The SCoPS simulation also shows useful skill in predicting the wintertime Arctic Oscillation. Consequently, SCoPS is more skillful than CCSM3 in predicting seasonal climate variability, including the ENSO and the Arctic Oscillation. Further, it is clear that the seasonal climate forecast with SCoPS will be useful for simulating the East Asian monsoon system.

Skillful Seasonal Forecasts of Summer Surface Air Temperature in Western China by Global Seasonal Forecast System Version 5

Variations of surface air temperature (SAT) are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT in western China, using the GloSea5 operational forecast system from the UK Met Office. Useful predictions are demonstrated, with considerable skill over most regions of western China. The temporal correlation coefficients of SAT between model predictions and observations are larger than 0.6, in both northwestern China and the Tibetan Plateau. There are two important sources of skill for these predictions in western China: interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific. The tropical SST change in the recent two decades, with a warming in the western Pacific and cooling in the eastern Pacific, which is reproduced well by the forecast system, provides a large contribution to the skill of SAT in northwestern China. Additionally, the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau. It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation. This process is evident irrespective of detrending both in observations and the model predictions, and acts as a source of skill in predictions for the Tibetan Plateau. The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits.

A geostatistical state-space model of animal densities for stream networks.

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but only marginally improved the density estimates in the spatiotemporal model. We applied this model to brook trout data from the West Susquehanna Watershed in Pennsylvania collected over 34yr from 1981 to 2014. We found the model including temporal and spatiotemporal autocorrelation best described young of the year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately high spatiotemporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatiotemporal correlation and higher temporal autocorrelation.

Origins of the Decadal Predictability of East Asian Land Summer Monsoon Rainfall

AbstractThe present study aims to explore the origins of decadal predictability of East Asian land summer monsoon rainfall (EA-LR) and estimate its potential decadal predictability. As a preliminary study, a domain-averaged EA-LR index (EA-LI) is targeted as it represents the leading mode of variability reasonably well. It is found that the decadal variations of EA-LI are primarily linked to a cooling over the central-eastern tropical Pacific (CEP) and a warming over the extratropical North Pacific and western tropical Pacific (NWP) during May–October. Two numerical experiments suggest that the CEP cooling may be a major driver of EA-LR, while the NWP warming, which is largely a response, cannot be treated as a forcing to EA-LR. However, this does not mean that the NWP sea surface temperature anomalies (SSTAs) play no role. To elaborate on this point, a third experiment is conducted in which the observed cooling is nudged in the CEP but the SST is nudged to climatology in the NWP (i.e., atmosphere–ocean interaction is not allowed). The result shows anomalous northerlies and decreased rainfall over East Asia. Results of the three experiments together suggest that both the forcings from the CEP and the atmosphere–ocean interaction in the NWP are important for EA-LR. Assuming that the tropical and North Pacific SSTAs can be “perfectly” forecasted, the so-called perfect prediction of EA-LI, which is achieved by a physics-based empirical model, yields a significant temporal correlation coefficient skill of 0.70 at a 7–10-yr lead time during a 40-yr independent hindcast (1968–2009), providing an estimation of the lower bound of potential decadal predictability of EA-LI.

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.