Linking the atmospheric Pacific-South American mode with oceanic variability and predictability

While Pacific climate variability is largely understood based on El Niño-Southern Oscillation (ENSO), the North Pacific focused Pacific decadal oscillation and the basin-wide interdecadal Pacific oscillation, the role of the South Pacific, including atmospheric drivers and cross-scale interactions, has received less attention. Using reanalysis data and model outputs, here we propose a paradigm for South Pacific climate variability whereby the atmospheric Pacific-South American (PSA) mode acts to excite multiscale spatiotemporal responses in the upper South Pacific Ocean. We find the second mid-troposphere PSA pattern is fundamental to stochastically generate a mid-latitude sea surface temperature quadrupole pattern that represents the optimal precursor for the predictability and evolution of both the South Pacific decadal oscillation and ENSO several seasons in advance. We find that the PSA mode is the key driver of oceanic variability in the South Pacific subtropics that generates a potentially predictable climate signal linked to the tropics.

A New Paradigm for South Pacific Climate Variability and Predictability

Pacific climate variability is largely understood based on El Niño–Southern Oscillation (ENSO), the North Pacific focused Pacific decadal oscillation (PDO) and/or the whole of Pacific region interdecadal Pacific oscillation – which respectively represent the dominant modes of interannual and decadal climate variability. However, the role of the South Pacific, including atmospheric drivers and cross-scale interactions between interannual and decadal climate variability, has received considerably less attention. Here we propose a new paradigm for South Pacific climate variability whereby the Pacific-South American (PSA) mode, characterised by two mid-tropospheric modes (PSA1 and PSA2), provides coherent noise forcing that acts to excite multiple spatiotemporal scales of oceanic responses in the upper South Pacific Ocean ranging from seasonal to decadal. While PSA1 has long been recognised as highly correlated with ENSO, we find that PSA2 is critically important in generating a sea surface temperature (SST) quadrupole pattern in the extratropical South Pacific. This sets up a precursor that optimally determines the predictability and evolution of SST 9 months in advance of the peak phases of both the leading South Pacific SST mode and ENSO. Our results show that the atmospheric PSA mode is the key driver of oceanic variability in the South Pacific subtropics.

Two Severe Prolonged Hydrological Droughts Analysis over Mainland Australia Using GRACE Satellite Data

In recent years, many droughts have happened over mainland Australia, especially the two severe prolonged droughts, from 2006 to 2009 and 2018 to 2020, resulting in serious water scarcity. Therefore, using the Total Storage Deficit Index (TSDI) from the Gravity Recovery and Climate Experiment (GRACE), we analyzed the two severe prolonged droughts from the perspective of the affected area, spatial evolution, frequency, severity and drought driving factors. The results show that the affected area of Drought 2006–2009 ranged from 57% to 95%, and that of Drought 2018–2020 ranged from 45% to 95%. Drought 2006–2009 took its rise in southeastern Australia and gradually spread to the central part. Drought 2018–2020 originated in the southwest corner of the Northern Territory and northern New South Wales, and gradually expanded to Western Australia and the whole New South Wales respectively. During Drought 2006–2009, Victoria suffered drought all months, including 59% mild drought and 41% moderate drought, North Territory had the highest drought severity of 44.26 and Victoria ranked the second high with the severity of 35.51 (cm months). For Drought 2018–2020, Northern Territory was also dominated by drought all months, including 92% mild drought and 8% moderate drought, the drought severities were in North Territory and Western Australia with 52.19 and 31.44 (cm months), respectively. Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed. By comparing the correlation coefficients of Drought 2018–2020 with Drought 2006–2009, we find that the impact of the El Niño on the hydrological drought becomes weaker while IOD is stronger, and the role of Southern Oscillation on droughts is diverse with the quite different spatial patterns. The results from Fourier analysis confirm that the two hydrological droughts are all related to Indo-Pacific climate variability but with slightly different driving mechanisms.

A method for predicting the uncompleted climate transition process

Climate change is expressed as a climate system transiting from the initial state to a new state in a short time. The period between the initial state and the new state is defined as the transition process, which is the key part for connecting the two states. By using a piece-wise function, the transition process is stated approximately (Mudelsee, 2000). However, the dynamic processes are not included in the piece-wise function. Thus, we proposed a method (Yan et al., 2015, 2016) to fit the transition process by using a continuous function. In this paper, this method is further developed for predicting the uncompleted transition process based on the dynamic characteristics of the continuous function. We introduce this prediction method in detail and apply it to three ideal time sequences and the Pacific Decadal Oscillation (PDO). The PDO is a long-lasting El Niño-like pattern of Pacific climate variability (Barnett et al., 1999; Newman et al., 2016). A new quantitative relationship during the transition process has been revealed, and it explores a nonlinear relationship between the linear trend and the amplitude (difference) between the initial state and the end state. As the transition process begins, the initial state and the linear trend are estimated. Then, according to the relationship, the end state and end moment of the uncompleted transition process are predicted.

Impact of Indo-Pacific Climate Variability on Rice Productivity in Bihar, India

The impact of Indo-Pacific climate variability in the South Asian region is very pronounced and their impact on agriculture is very important for the Indian subcontinent. In this study, rice productivity, climatic factors (Rainfall, Temperature and Soil Moisture) and associated major Indo-Pacific climate indices in Bihar were investigated. Bihar is one of the major rice-producing states of India and the role of climate variability and prevailing climate indices in six events (between 1991–2014) with severer than −10% rice productivity are analyzed. The Five-year moving average, Pearson’s Product Moment Correlation, Partial Correlation, Linear Regression Model, Mann Kendall Test, Sen’s Slope and some other important statistical techniques were used to understand the association between climatic variables and rice productivity. Pearson’s Product Moment Correlation provided an overview of the significant correlation between climate indices and rice productivity. Whereas, Partial Correlation provided the most refined results on it and among all the climate indices, Niño 3, Ocean Niño Index and Southern Oscillation Index are found highly associated with years having severer than −10% decline in rice productivity. Rainfall, temperature and soil moisture anomalies are analyzed to observe the importance of climate factors in rice productivity. Along with the lack of rainfall, lack of soil moisture and persistent above normal temperature (especially maximum temperature) are found to be the important factors in cases of severe loss in rice productivity. Observation of the dynamics of ocean-atmosphere coupling through the composite map shows the Pacific warming signals during the event years. The analysis revealed a negative (positive) correlation of rice productivity with the Niño 3 and Ocean Niño Index (Southern Oscillation Index).