Pacific Decadal Oscillation Index Research Articles

Shortwave Radiative Flux Variability Through the Lens of the Pacific Decadal Oscillation

AbstractThe variability of the shortwave radiative fluxes at the surface and top of atmosphere (TOA) is examined in a pre‐industrial modeling setup using the Pacific Decadal Oscillation (PDO) as a possible pacemaker of atmospheric decadal‐scale variability. Within models from the Coupled Model Intercomparison Project—Phase 6, downwelling shortwave radiation at the surface, the net shortwave fluxes at the surface and TOA, as well as cloud radiative effects show remarkably similar patterns associated with the PDO. Through ensemble simulations designed with a pure PDO pattern in the North Pacific only, we show that the PDO relates to about 20%–40% of the unforced year‐to‐year variability of these shortwave fluxes over the Northern Hemispheric continents. The sea surface temperature imprint on shortwave‐flux variability over land is larger for spatially aggregated time series as compared to smaller areas, due to the blurring effect of small‐scale atmospheric noise. The surface and TOA radiative flux anomalies associated with the PDO index range of [−1.64; 1.64] are estimated to reach up to ±6 Wm−2 for North America, ∓3 Wm−2 for India and ±2 Wm−2 for Europe. We hypothesize that the redistribution of clouds in response to a North Pacific PDO anomaly can impact the South Pacific and North Atlantic SSTs.

The Relationship between the Typhoons Affecting South China and the Pacific Decadal Oscillation

Using typhoon data from the Shanghai Typhoon Institute of the China Meteorological Administration, the Japan Meteorological Agency’s annual Pacific decadal oscillation (PDO) index, and NCEP/NCAR reanalysis data from 1951 to 2021, correlation and composite analyses were carried out to study the relationship between the variability among tropical cyclones of different magnitudes affecting South China and the PDO. The results show that there is an obvious out-of-phase relationship between the proportion of tropical cyclones reaching a typhoon-level intensity or above in South China and the PDO index. When the PDO is in a cold (warm) phase, the sea surface temperature in the eastern and central equatorial Pacific is cold (warm), similar to the eastern Pacific La Niña (El Niño) phenomenon, and the SST in the eastern and western tropical Pacific Ocean shows a negative (positive) gradient; the subtropical high in the western Pacific Ocean is weaker (stronger) than normal, with the western ridge point to the east (west), and the 500 hPa geopotential height in the South China Sea and the area east of the Philippines is weaker (stronger), which is conducive to (unfavorable to) the formation of a monsoon trough; and the westerly (easterly) winds at high altitudes and the southwesterly (northeasterly) winds at low altitudes from the South China Sea to the Philippines are abnormally strong, and a positive (negative) vorticity at low altitudes, a low (high) sea level pressure, and strong (weak) convection are shown. These conditions are favorable (unfavorable) for the intensification of typhoons affecting South China, and as a result, the number of tropical cyclones reaching the level of typhoons or above account for a greater (smaller) proportion of those affecting South China.

Detecting shifts in nonlinear dynamics using Empirical Dynamic Modeling with Nested-Library Analysis.

Abrupt changes in system states and dynamical behaviors are often observed in natural systems; such phenomena, named regime shifts, are explained as transitions between alternative steady states (more generally, attractors). Various methods have been proposed to detect regime shifts from time series data, but a generic detection method with theoretical linkage to underlying dynamics is lacking. Here, we provide a novel method named Nested-Library Analysis (NLA) to retrospectively detect regime shifts using empirical dynamic modeling (EDM) rooted in theory of attractor reconstruction. Specifically, NLA determines the time of regime shift as the cutting point at which sequential reduction of the library set (i.e., the time series data used to reconstruct the attractor for forecasting) optimizes the forecast skill of EDM. We illustrate this method on a chaotic model of which changing parameters present a critical transition. Our analysis shows that NLA detects the change point in the model system and outperforms existing approaches based on statistical characteristics. In addition, NLA empirically detected a real-world regime shift event revealing an abrupt change of Pacific Decadal Oscillation index around the mid-1970s. Importantly, our method can be easily generalized to various systems because NLA is equation-free and requires only a single time series.

Constraint of Air–Sea Interaction Significant to Skillful Predictions of North Pacific Climate Variations

Abstract The Pacific decadal oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air–sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions. Significance Statement The Pacific decadal oscillation is a prominent decadal climate variability over the North Pacific. However, accurately predicting the Pacific decadal oscillation remains a challenge. In this study, we use an advanced initialization method where the oceanic observations are incorporated into a climate model constrained by air–sea interactions. We can successfully predict the Pacific decadal oscillation up to 10 years in advance, which is hardly achieved by the state-of-the-art climate prediction systems. Our results suggest that the constraint of air–sea interaction during initialization is important to skillful predictions of the climate variability on decadal time scales.

Pacific Decadal Oscillation Forecasting With Spatiotemporal Embedding Network

AbstractThe Pacific decadal oscillation (PDO) is a decadal variability phenomenon occurring in the North Pacific Ocean. It has substantial impacts on marine ecosystems and the global climate. Due to the high complexity and unclear evolution mechanism, the accurate long‐term prediction of PDO remains a challenge. In this paper, a deep spatiotemporal embedding network (DSEN) is proposed to extract the spatiotemporal features from historical climate data and achieve end‐to‐end forecasting of the PDO index. The spatiotemporal features are recursive in the continuous forecasting of the PDO index on seasonal time scales, thus the cumulative error is largely reduced. During the test period of 39 years (1982–2020), our model can skillfully predict the PDO index up to 1 year, outperforming six methods used as benchmark. By contrast with physically‐based methods, DSEN can accurately predict the PDO index from a data‐driven perspective.

Spatiotemporal variation and multivariate controls of short‐cycle drought–flood abrupt alteration: A case in the Qinling‐Daba Mountains of China

AbstractA comprehensive understanding of the patterns of drought and flood alternation in adjacent months is essential for enabling climate adaptation and mitigation strategies. However, there is a paucity of knowledge regarding short‐cycle drought–flood abrupt alteration (s‐DFAA) and its responses to multiple environmental factors at various timescales. This is because of the inadequate formula construction of the existing short‐cycle drought–flood abrupt alteration index (SDFAI), such as the inability to introduce drought standards. To accurately capture the s‐DFAA's characteristics in the Qinling‐Daba Mountains (Qinba Mountains), we proposed a revised SDFAI (R‐SDFAI), which incorporates the standardized precipitation index (SPI) and allows for the customization of drought standards. Next, we used wavelet transform coherence and multiple wavelet coherence to investigate the timescale relationships between s‐DFAA events and their influence factors such as relative humidity, sunshine duration, temperature, evaporation, Arctic Oscillation (AO), Niño3.4 SSTA Index (Niño3.4), Total Sunspot Number Index (TSNI) and Pacific Decadal Oscillation Index (PDO). Our results showed that the R‐SDFAI outperformed traditional SDFAI in capturing s‐DFAA events and characterizing their severity. Furthermore, s‐DFAA events identified by R‐SDFAI at different levels (i.e., mild, moderate, severe, extreme and total) displayed insignificant downward trends. Spatially, there were more s‐DFAA events in the east than the west. Wavelet analysis indicated that meteorological factors and teleconnections significantly impact s‐DFAA events at large timescales, though their driving mechanisms differed substantially. Among meteorological factors, single relative humidity and its related combinations exhibited relatively high percent area of significant coherence (PASC, ranging from 17.47 to 29.46). Each teleconnection and its combinations are irreplaceable, with PASC values always increasing with the number of variables. The PASC ranges from 8.3 to 10.29 for one factor, 12.38 to 18.65 for two factors, 24.02 to 30.58 for three factors and 40.88 for four factors, respectively.

Controlling factor analysis of oceanic surface pCO2 in the South China Sea using a three-dimensional high-resolution biogeochemical model

The oceanic surface pressure of CO2 (pCO2) is an essential parameter for understanding the global and regional carbon cycle and the oceanic carbon uptake capacity. We constructed a three-dimensional physical-biogeochemical model with a high resolution of 1/30° for the South China Sea (SCS) to compensate for the limited temporal coverage and limited spatial resolution of the observations and numerical models. The model simulated oceanic surface pCO2 from 1992 to 2021, and the empirical orthogonal function analysis (EOF) of the model results is conducted for a better understanding of the seasonal and interannual variations of oceanic surface pCO2 in this region. The model results showed that the SCS serves as an atmospheric CO2 source from March to October and a sink from November to February, with a domain-averaged climatological oceanic surface pCO2 value that varies between 357 and 408 μatm, and the temporal variation was positively correlated with the variation of sea surface temperature (SST). The majority of the SCS showed a long-term increasing trend for oceanic surface pCO2 with a value of (1.19±0.60) μatm/a, which is in response to the continuously rising atmospheric CO2 concentration. The first EOF mode is positively correlated with the Niño 3 index with a correlation coefficient of 0.51 when the Niño 3 leads 5 months, and the second EOF mode is correlated with the PDO index when the PDO leads 7 months, which suggests an influence of climate variability on the carbonate system. Moreover, it was found that the long-term trend rate of oceanic surface pCO2 was mainly controlled by total CO2 (TCO2) through the decomposition of influence factors, and SST variation took a dominant role in seasonal variations of pCO2. With rapid global warming and continuous release of CO2, the carbonate system in the SCS may change leading to calcite and aragonite saturation.

Protist dynamics in the eastern Tsugaru Strait, Japan from 2010 to 2018: Implications for the relationship between decadal climatology and aquaculture production

Environmental changes such as seasonality, decadal oscillation, and anthropogenic forcing may shape the dynamics of lower trophic-level organisms. In this study, 9-years (2010–2018) of monitoring data on microscopic protists such as diatoms and dinoflagellates, and environmental variables were analyzed to clarify the relationships between plankton and local/synoptic environmental changes. We found that time-series temperature increased in May, whereas it decreased in August and November. Nutrients (e.g., phosphate) decreased in May, remained unchanged in August, and increased in November from 2010 to 2018. The partial pressure of CO2 increased in May, August, and November over time. It is notable that the change in seawater temperature (−0.54 to 0.32 °C per year) and CO2 levels (3.6–5.7 μatm CO2 per year) in the latest decade in the eastern Tsugaru Strait were highly dynamic than the projected anthropogenic climate change. Protist abundance generally increased or stayed unchanged during the examined period. In August and November, when cooling and decreases in pH occurred, diatoms such as Chaetoceros subgenus Hyalochaete spp. and Rhizosoleniaceae temporally increased from 2010 to 2018. During the study period, we found that locally aquacultured scallops elevated soft tissue mass relative to the total weight as diatom abundance increased, and the relative scallop soft tissue mass was positively related to the Pacific Decadal Oscillation index. These results indicate that decadal climatic forcing in the ocean modifies the local physical and chemical environment, which strongly affects phytoplankton dynamics rather than the effect of anthropogenic climate change in the eastern Tsugaru Strait.

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

Abrupt transitions in a southwest USA desert grassland related to the Pacific Decadal Oscillation.

Prediction of abrupt ecosystem transitions resulting from climatic change will be an essential element of adaptation strategies in the coming decades. In the arid southwest USA, the collapse and recovery of long-lived perennial grasses have important effects on ecosystem services, but the causes of these variations have been poorly understood. Here we use a quality-controlled vegetation monitoring dataset initiated in 1915 to show that grass cover dynamics during the 20th century were closely correlated to the Pacific decadal oscillation (PDO) index. The relationship out-performed models correlating grasses to yearly precipitation and drought indices, suggesting that ecosystem transitions attributed only to local disturbances were instead influenced by climate teleconnections. Shifts in PDO phase over time were associated with the persistent loss of core grass species and recovery of transient species, so recovery of grasses in aggregate concealed significant changes in species composition. However, the relationship between PDO and grass cover broke down after 1995; grass cover is consistently lower than PDO would predict. The decoupling of grass cover from the PDO suggests that a threshold had been crossed in which warming or land degradation overwhelmed the ability of any grass species to recover during favorable periods.

Characterizing and forecasting climate indices using time series models

The objective of the current study is to present a comparison of techniques for the forecasting of low-frequency climate oscillation indices with a focus on the Great Lakes system. A number of time series models have been tested including the traditional autoregressive moving average (ARMA) model, dynamic linear model (DLM), generalized autoregressive conditional heteroskedasticity (GARCH) model, as well as the nonstationary oscillation resampling (NSOR) technique. These models were used to forecast the monthly El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) indices which show the most significant teleconnection with the net basin supply (NBS) of the Great Lakes system from a preliminary study. The overall objective is to predict future water levels, ice extent, and temperature, for planning and decision making purposes. The results showed that the DLM and GARCH models are superior for forecasting the monthly ENSO index, while the forecasted values from the traditional ARMA model presented a good agreement with the observed values within a short lead time ahead for the monthly PDO index.

Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea

AbstractAccurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Niño–southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Niño 3.4, and Southern Oscillation Index (SOI) for the period of 1981–2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981–2009) and tested with 12 years of data (2009–2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach.

Amplitude modulations of seasonal variability in the Karimata Strait throughflow

The Karimata Strait (KS) throughflow between the South China Sea (SCS) and Java Sea plays an essential role in heat and freshwater budget in the SCS and dual roles in strengthening/reducing the primary Indonesian throughflow (ITF) in the Makassar Strait. A sustained long-term monitoring of the ITF is logistically challenging and expensive; therefore, proxies are needed. Here, we use a combination of in situ measurement of the KS throughflow and satellite-derived sea surface height (SSH) and sea surface wind (SSW) to determine the interannual and decadal modulations in seasonal amplitude of the KS throughflow associated with El Niño-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Pacific Decadal Oscillation (PDO). Linear regression, correlation, harmonic and power spectrum analyses are used. The results manifest that there are significant interannual to decadal modulations in the seasonal amplitude of the KS throughflow. The modulations of the seasonal amplitude in the volume and heat transports range 1.36-1.92 Sv (1 Sv = 106 m3 s-1) and 126.41-173.36 TW (1 TW = 1012 W), respectively, with a significant cycle of ~9 years. From 1994 to 2020, the seasonal amplitude of volume transport through the KS shows an increasing trend of 37.75 ± 15.69 mSv decade-1 (1 mSv = 103 m3 s-1). The seasonal amplitude of the heat transport also increases, at a rate of 4.78 ± 1.52 TW decade-1. The KS volume transport is positively correlated with PDO and ENSO indices (r2 = 0.69 and r2 = 0.58), with a lag of 12 and 10 months, respectively. The results of composite analysis suggest that the interannual variability of the KS transport is related to the interannual anomalies of the SSH gradient and the local SSW fields in boreal winter.

Evaluation of the Pacific Decadal Oscillation from 1901 to 2014 in CMIP6 models

The simulation of internal climate variability is key to understanding climate change. Internal fluctuations can fully obscure or amplify the underlying climate-change signal in many fields over years to decades. The Pacific Decadal Oscillation (PDO) is an important mechanism that dominates interdecadal climate variations. Here, the capacity of 36 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for reproducing the PDO during the period 1901-2014 was evaluated across spatial pattern, amplitude, periodicity and phase aspects. The results suggested that approximately 71% of the models were capable of capturing the spatial pattern of the PDO. However, most of the other models underestimated the percentage of the variance explained and the pattern amplitude. CMIP6 models can capture the periodicity over shorter periods (<20 yr), while most CMIP6 models underestimate the periodicity of the PDO on the interdecadal scale (60 yr periods). In particular, the probability distribution functions (PDFs) of the raw PDO index showed that most CMIP6 models could capture the unimodal probability distribution. Some of the models were able to reproduce the bimodal probability distribution of the PDO index filtered by the ensemble empirical mode decomposition method. Moreover, the findings also showed that the multimodel ensemble (MME) for 36 CMIP6 models could effectively capture the spatial pattern and periodicity over a shorter period (<20 yr) of the PDO. Moreover, the MME exhibited higher fidelity to the observation in the PDF of the PDO index, although the positive area was larger than the observation. These findings are crucial because they may help detect simulation gaps and provide valuable references for the design and applicability of future models worldwide.

Across the North Pacific, dietary-induced stress of breeding rhinoceros auklets increases with high summer Pacific Decadal Oscillation index

Multi-colony studies of breeding seabirds may provide insights into the mechanistic links between large-scale climate variability and local changes in prey availability. In the North Pacific, the Pacific Decadal Oscillation (PDO) is a dominant climate index characterized by contrasting patterns in sea surface temperature between the western and eastern North Pacific. To examine how inter-annual variability in the PDO affects rhinoceros auklets Cerorhinca monocerata across the North Pacific, we measured inter-annual changes in nutritional stress (as reflected in plasma levels of corticosterone) of adults breeding on 5 colonies (2 and 3 colonies from the western and eastern Pacific, respectively). We also examined concurrent changes in mass and energy content of food loads delivered to chicks. We found that higher summer PDO values were associated with increased corticosterone levels and lower mass and energy contents of the food loads in both the western and eastern North Pacific colonies. Results indicated that oceanographic conditions during higher PDO values induced local changes in forage fish communities, leading to reduced prey availability, which, in turn, increased the nutritional stress of breeding birds. We conclude that the higher summer PDO index in recent years was associated with food-poor conditions for breeding rhinoceros auklets across their reproductive range, and prolonged periods of high summer PDO may be detrimental to the populations of this seabird species. Our results highlight the complexity of the mechanisms of how large-scale climate variability affects seabirds with a large geographical distribution.

The Influence of Spatial and Temporal Scales on Fisheries Modeling—An Example of Sthenoteuthis oualaniensis in the Nansha Islands, South China Sea

The choice of spatial and temporal scales affects the performance of fisheries models and is particularly important in exploring the relationship between resource abundance and the marine environment. Traditional fishery models are constructed at a particular scale, and the results of the study hold only at that scale. Sthenoteuthis oualaniensis is one of the main target species of large-scale light falling-net fishing in the Nansha Islands in the South China Sea. We used the S. oualaniensis fishery in the Nansha Islands as an example to compare the performance of fisheries models for 12 spatial and temporal settings and to explore the relationship between the abundance of S. oualaniensis and the marine environment in the Nansha Islands under the optimal spatial and temporal settings. The results show that the spatial and temporal scale chosen in the construction of the fishery model is not as fine as possible in generalized additive models (GAMs) for abundance index-catch per unit effort (AI-CPUE)-based scenarios, and 0.5° with the season was the best spatial and temporal setting; meanwhile, in GAMs for AI-effort-based scenarios, 0.1° with the month was the best spatial and temporal setting. The distribution of S. oualaniensis resources in the Nansha Islands was characterized by significant seasonal variation, and the monthly center of gravity had a significant negative correlation with the Niño 3.4 index and the PDO index, with correlation coefficients of 100 and 1000, respectively. It is hypothesized that Pacific Decadal Oscillation and ENSO events affect the marine environment in the South China Sea by influencing the strength of the Kuroshio force and the degree of Kuroshio curvature, which in turn affects the distribution of S. oualaniensis in the Nansha Islands. The results help us to understand the influence of spatial and temporal scales on fisheries models and the environmental factors affecting the distribution of S. oualaniensis resources in the Nansha Islands. Thus, they provide a scientific basis for the sustainable development of S. oualaniensis fisheries in this region.

Response of Spatial and Temporal Variations in the Kuroshio Current to Water Column Structure in the Western Part of the East Sea

Using geographic sea surface current data, long-term changes in spatial and temporal variations in the Kuroshio Current 1993–2021 were analyzed, and the relationship between the Kuroshio Current and oceanic conditions, such as water column structure and intensity of East Korea Warm Current (EKWC) in the western part of the East Sea (WES), was investigated. Long-term changes in the Kuroshio Current intensity were positively correlated with the Pacific Decadal Oscillation and East Asian Winter Monsoon indices. When the Kuroshio Current was strong, its main axis passing around the Ryukyu Islands moved eastward, and the intensity of EKWC separated from the Kuroshio Current and flowed into the WES, indicating weakened conditions. When the intensity of the EKWC was weakened, its main axis moved away from the inshore area of the WES. As a result, the vertical distribution range of the cold and low saline water mass located in the bottom layer extended to shallower depths in the inshore area of the WES with increasing chlorophyll-a.

A Place-Based Approach to Drought Forecasting in South-Central Oklahoma.

To assist water managers in south-central Oklahoma prepare for future drought, reliable place-based drought forecasts are produced. Past-, present-, and future-forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the periodJanuary 1901 to November 2021, a second-order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross-validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best-fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R-squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought.

Variations in Phytoplankton Primary Production Driven by the Pacific Decadal Oscillation in the East/Japan Sea

AbstractThe Pacific Decadal Oscillation (PDO) regime is a major factor not only for the physical properties of the ocean but also for fishery and water resources. However, only a few studies have examined the impact of the PDO on the marine ecosystem in the East/Japan Sea. Therefore, in this study, the relationship between PDO and primary production (PP), and subsequent effects on the marine ecosystem were investigated in the East/Japan Sea using satellite data sets. PDO index showed a negative relationship with sea surface temperature (SST) and the contribution of the small phytoplankton to the total PP during the study period, whereas the mixed layer depth (MLD) and the PP showed a positive relationship with PDO index. The shallower MLD during the negative PDO phase indicates that vertical mixing may be weakened due to the stronger stratification caused by the higher SST than observed during the positive PDO phase. Consequently, we hypothesized that weakened vertical mixing may reduce nutrient supply to the euphotic layer, providing small‐sized phytoplankton favored environmental conditions during the negative PDO. It is noteworthy that PDO‐induced shoaling of the MLD was mainly observed in winter, which may influence the annual PP of the following year. This study shows that the annual PP in the East/Japan Sea can be largely affected through interactions between SST, MLD and subsequent changes in nutrient regime according to the PDO regime, which subsequently affects potential fishery resources in the East/Japan Sea.

Did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans?

The sea surface temperature (SST), anomalies (SSTA), Oceanic Niño Index (ONI), and Multivariate ENSO (MEI) index in El Niño 1 + 2 and El Niño 3.4 regions, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO), as well as the meteorological parameter Southern Oscillation Index (SOI) were cross correlated to sunspot counts (SS) from cycles 19 to 24 (1954–2019). Over the 1954–2019 period, the SS or Schwabe cycles did not show strong cross-correlation coefficients (cc-ρ) with values falling between 0.063 and 0.100 ( p < 0.05). It seems that the Total Solar Irradiation (TSI) constant variability (±0.1%, ±1.361 W m −2 ) due to the SS cycles balanced out throughout the whole period. The cc-ρ coefficients for SST and SSTA versus each individual SS cycles were between 0.100 and 0.200 with lag times between maxima of each being from a few to 48 months. The ONI showed a cc-ρ < 0.1, while MEI reached up to 0.2 through all SS cycles. The slope of the cc-ρ changed from negative to positive over 6–12 months periods, with peaks in slope change occurring somewhere between 2 and 3 years. The SOI cc-ρ varied by around 0.200 through cycles 19–21, but for SS 22–24 it was not noticeable. Interdecadal indexes PDO and AMO showed cc-ρ of up to 0.283; with a possible association of 8%, with a lag time of 1–3 years. During the ascending and descending phases of each SS cycle, the cc-ρ were 0.288 and − 0.233, respectively for SST in El Niño 3.4 region, but in El Niño 1 + 2 were negligible. The ONI and MEI showed cc-ρ up to 0.448 and 0.480 respectively with lag times of 1–15 months in ascending phase of the SS cycle. The SS vs SOI had cc- ρ up to +0.420 to −0.567 in the ascending phases, while PDO and AMO registered cc-ρ up to 0.417 and − 0.491. AMO appeared systematically associated with SS cycles from 10.0 to 30.2% during descending and ascending phases, respectively. Compiled SS counts for all the ascending and descending phases of the SS cycles, gave a clear spectral coherence (quasi sine-function distribution); for SST and SSTA, lag times of 9–48 months were observed with inverse and direct linear relationship, and peaks of −0.38 and 0.39, respectively. The ONI and MEI with SS counts have similar cc-ρ values: 0.245 and 0.387. Around 6–15% of the ONI and MEI can be explained by SS during ascending and < 4% in descending phases. It seems that over the relatively short time scales of SS cycles, either on their initial ascending or final descending phases, the studied indexes seem to be influenced. Even though it was difficult to elucidate the physics behind the observed cross-correlations, these results can be used to help improve understanding and aid the predictions of ENSO, PDO and AMO oceanographic events in the Pacific and Atlantic oceans. • Sea surface temperature is affected by sun energy that varies in time due to astronomical factors and sun internal activity • The sun activity can be estimated by sun spots (SS) • Inter-annual El/La Niño/Niña as well as decadal processes should be affected by SS • It was found weak to moderate cross-correlation between SS and oceanographic indexes from 1954 to 2019 • This fact should be considered to improve skill modelling forecasts of such indexes