Early warning of the Indian Ocean Dipole using climate network analysis.

SignificanceThe Indian Ocean Dipole (IOD), an air-sea coupled phenomenon over the tropical Indian Ocean, has substantial impacts on the climate, ecosystems, and society. Due to the winter predictability barrier, however, a reliable prediction of the IOD has been limited to 3 or 4 mo in advance. Our work approaches this problem from a new data-driven perspective: the climate network analysis. Using this network-based method, an efficient early warning signal for the IOD event was revealed in boreal winter. Our approach can correctly predict the IOD events one calendar year in advance (from December of the previous year) with a hit rate of higher than 70%, which strongly outperforms current dynamic models.

Biomass responses in a temperate European grassland through 17 years of elevated CO2.

Future increase in atmospheric CO2 concentrations will potentially enhance grassland biomass production and shift the functional group composition with consequences for ecosystem functioning. In the "GiFACE" experiment (Giessen Free Air Carbon dioxide Enrichment), fertilized grassland plots were fumigated with elevated CO2 (eCO2 ) year-round during daylight hours since 1998, at a level of +20% relative to ambient concentrations (in 1998, aCO2 was 364 ppm and eCO2 399 ppm; in 2014, aCO2 was 397 ppm and eCO2 518 ppm). Harvests were conducted twice annually through 23 years including 17 years with eCO2 (1998 to 2014). Biomass consisted of C3 grasses and forbs, with a small proportion of legumes. The total aboveground biomass (TAB) was significantly increased under eCO2 (p = .045 and .025, at first and second harvest). The dominant plant functional group grasses responded positively at the start, but for forbs, the effect of eCO2 started out as a negative response. The increase in TAB in response to eCO2 was approximately 15% during the period from 2006 to 2014, suggesting that there was no attenuation of eCO2 effects over time, tentatively a consequence of the fertilization management. Biomass and soil moisture responses were closely linked. The soil moisture surplus (c. 3%) in eCO2 manifested in the latter years was associated with a positive biomass response of both functional groups. The direction of the biomass response of the functional group forbs changed over the experimental duration, intensified by extreme weather conditions, pointing to the need of long-term field studies for obtaining reliable responses of perennial ecosystems to eCO2 and as a basis for model development.

Evaluation of ENSO simulations in CMIP5 models: A new perspective based on percolation phase transition in complex networks.

In this study, the performance of CMIP5 models in simulating the El Niño-Southern Oscillation (ENSO) is evaluated by using a new metric based on percolation theory. The surface air temperatures (SATs) over the tropical Pacific Ocean are constructed as a SAT network, and the nodes within the network are linked if they are highly connected (e.g., high correlations). It has been confirmed from reanalysis datasets that the SAT network undergoes an abrupt percolation phase transition when the influences of the sea surface temperature anomalies (SSTAs) below are strong enough. However, from simulations of the CMIP5 models, most models are found incapable of capturing the observed phase transition at a proper critical point Pc. For the 15 considered models, four even miss the phase transition, indicating that the simulated SAT network is too stable to be significantly changed by the SSTA below. Only four models can be considered cautiously with some skills in simulating the observed phase transition of the SAT network. By comparing the simulated SSTA patterns with the node vulnerabilities, which is the chance of each node being isolated during a ENSO event, we find that the improperly simulated sea-air interactions are responsible for the missing of the observed percolation phase transition. Accordingly, a careful study of the sea-air couplers, as well as the atmospheric components of the CMIP5 models is suggested. Since the percolation phase transition of the SAT network is a useful phenomenon to indicate whether the ENSO impacts can be transferred remotely, it deserves more attention for future model development.

Extreme climatic events down-regulate the grassland biomass response to elevated carbon dioxide.

Terrestrial ecosystems are considered as carbon sinks that may mitigate the impacts of increased atmospheric CO2 concentration ([CO2]). However, it is not clear what their carbon sink capacity will be under extreme climatic conditions. In this study, we used long-term (1998-2013) data from a C3 grassland Free Air CO2 Enrichment (FACE) experiment in Germany to study the combined effects of elevated [CO2] and extreme climatic events (ECEs) on aboveground biomass production. CO2 fertilization effect (CFE), which represents the promoted plant photosynthesis and water use efficiency under higher [CO2], was quantiffied by calculating the relative differences in biomass between the plots with [CO2] enrichment and the plots with ambient [CO2]. Down-regulated CFEs were found when ECEs occurred during the growing season, and the CFE decreases were statistically significant with p well below 0.05 (t-test). Of all the observed ECEs, the strongest CFE decreases were associated with intensive and prolonged heat waves. These findings suggest that more frequent ECEs in the future are likely to restrict the mitigatory effects of C3 grassland ecosystems, leading to an accelerated warming trend. To reduce the uncertainties of future projections, the atmosphere-vegetation interactions, especially the ECEs effects, are emphasized and need to be better accounted.

Increase of the Antarctic Sea Ice Extent is highly significant only in the Ross Sea.

In the context of global warming, the question of why Antarctic sea ice extent (SIE) has increased is one of the most fundamental unsolved mysteries. Although many mechanisms have been proposed, it is still unclear whether the increasing trend is anthropogenically originated or only caused by internal natural variability. In this study, we employ a new method where the underlying natural persistence in the Antarctic SIE can be correctly accounted for. We find that the Antarctic SIE is not simply short-term persistent as assumed in the standard significance analysis, but actually characterized by a combination of both short- and long-term persistence. By generating surrogate data with the same persistence properties, the SIE trends over Antarctica (as well as five sub-regions) are evaluated using Monte-Carlo simulations. It is found that the SIE trends over most sub-regions of Antarctica are not statistically significant. Only the SIE over Ross Sea has experienced a highly significant increasing trend (p = 0.008) which cannot be explained by natural variability. Influenced by the positive SIE trend over Ross Sea, the SIE over the entire Antarctica also increased over the past decades, but the trend is only at the edge of being significant (p = 0.034).

Percolation Phase Transition of Surface Air Temperature Networks: A new test bed for El Niño/La Niña simulations.

In this work, we studied the air-sea interaction over the tropical central eastern Pacific from a new perspective, climate network. The surface air temperatures over the tropical Pacific were constructed as a network, and the nodes within this network were linked if they have a similar temporal varying pattern. Using three different reanalysis datasets, we verified the percolation phase transition. That is, when the influences of El Niño/La Niña are strong enough to isolate more than 48% of the nodes, the network may abruptly be divided into many small pieces, indicating a change of the network state. This phenomenon was reproduced successfully by a coupled general circulation model, Flexible Global Ocean-Atmosphere-Land System Model Spectral Version 2, but another model, Flexible Global Ocean-Atmosphere-Land System Model Grid-point Version 2, failed. As both models have the same oceanic component, but are with different atmospheric components, the improperly used atmospheric component should be responsible for the missing of the percolation phase transition. Considering that this new phenomenon is only recently noticed, current state-of-the-art models may ignore this process and induce unrealistic simulations. Accordingly, percolation phase transition is proposed as a new test bed, which deserves more attention in the future.

"Intrinsic" correlations and their temporal evolutions between winter-time PNA/EPW and winter drought in the west United States.

In this study, relations between winter-time Pacific-Northern America pattern (PNA)/East Pacific wave-train (EPW) and winter-time drought in the west United States over the period of 1951-2010 are analyzed. Considering traditional Pearson's Correlation Coefficient can be influenced by non-stationarity and nonlinearity, a recently proposed method, Detrended Partial-Cross-Correlation Analysis (DPCCA) is applied. With DPCCA, we analyzed the "intrinsic" correlations between PNA/EPW and the winter drought with possible effects of ENSO and PDO removed. We found, i) significant negative correlations between PNA/EPW and drought on time scales of 5-6 years after removing the effects of ENSO, ii) and significant negative correlations between PNA/EPW and drought on time scales of 15-25 years after removing the effects of PDO. By further studying the temporal evolutions of the "intrinsic" correlations, we found on time scales of 5-6 years, the "intrinsic" correlations between PNA/EPW and drought can vary severely with time, but for most time, the correlations are negative. While on interdecadal (15-25 years) time scales, after the effects of PDO removed, unlike the relations between PNA and drought, the "intrinsic" correlations between EPW and drought takes nearly homogeneous-sign over the whole period, indicating a better model can be designed by using EPW.

Percolation Phase Transition of Surface Air Temperature Networks under Attacks of El Niño/La Niña.

In this study, sea surface air temperature over the Pacific is constructed as a network, and the influences of sea surface temperature anomaly in the tropical central eastern Pacific (El Niño/La Niña) are regarded as a kind of natural attack on the network. The results show that El Niño/La Niña leads an abrupt percolation phase transition on the climate networks from stable to unstable or metastable phase state, corresponding to the fact that the climate condition changes from normal to abnormal significantly during El Niño/La Niña. By simulating three different forms of attacks on an idealized network, including Most connected Attack (MA), Localized Attack (LA) and Random Attack (RA), we found that both MA and LA lead to stepwise phase transitions, while RA leads to a second-order phase transition. It is found that most attacks due to El Niño/La Niña are close to the combination of MA and LA, and a percolation critical threshold Pc can be estimated to determine whether the percolation phase transition happens. Therefore, the findings in this study may renew our understandings of the influence of El Niño/La Niña on climate, and further help us in better predicting the subsequent events triggered by El Niño/La Niña.

A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables.

In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time scales and over different periods. To illustrate their properties, we used two climatological examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are non-significant between 1865-1875. As for SRYR and PDO, significant correlations are found on time scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system, which compared to traditional methods, can objectively show how two time series are related (on which time scale, during which time period). These are important not only for diagnosis of complex system, but also for better designs of prediction models. Therefore, the new methods offer new opportunities for applications in natural sciences, such as ecology, economy, sociology and other research fields.

Beyond Benford's Law: Distinguishing Noise from Chaos.

Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly.

Detrended partial-cross-correlation analysis: a new method for analyzing correlations in complex system.

In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation technique, which can be applied to quantify the relations of two non-stationary signals (with influences of other signals removed) on different time scales. We illustrate the advantages of this method by performing two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II reveals the "intrinsic" relations between two considered time series with potential influences of other unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and Nino3-SSTA on time scales of 6 ~ 8 years are found over the period 1951 ~ 2012, while significant correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable results, we have confidence that DPCCA is an useful method in addressing complex systems.

Extracting climate memory using Fractional Integrated Statistical Model: a new perspective on climate prediction.

Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a new method, with which one can estimate the long-lasting influences of historical climate states on the present time quantitatively, and further extract the influence as climate memory signals. To show the usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate memory signals indeed can be extracted and the whole variations can be further decomposed into two parts: the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the larger proportion the climate memory signals will account for in the whole variations. With the climate memory signals extracted, one can at least determine on what basis the considered time series will continue to change. Therefore, this report provides a new perspective on climate prediction.