Empirical estimation of marine phytoplankton assemblages in coastal and offshore areas using an in situ multi-wavelength excitation fluorometer

Phytoplankton assemblages are important for understanding the quality of primary production in marine ecosystems. Here, we describe development of a methodology for monitoring marine phytoplankton assemblages using an in situ multi-wavelength excitation fluorometer (MEX) and its application for seasonal observations in coastal and offshore areas around Japan. The MEX recorded the fluorescence excited with nine light-emitting diodes, temperature, and sensor depth. We prepared reference datasets comprising temperature, MEX fluorescence, and plant-pigment-based chemotaxonomy phytoplankton assemblages. Target MEX fluorescence was decomposed by reference MEX fluorescence using a linear inverse model for calculating coefficients after the reference data were limited by temperature, followed by reconstruction of plant-pigment-based chemotaxonomy of the target MEX fluorescence using the coefficients and the chemotaxonomy assemblages of the reference data. Sensitivity analysis indicated poor estimation of the proportion and/or chlorophyll a-based abundance of chlorophytes, haptophytes, prasinophytes, and prochlorophytes; however, limiting the estimations to five chemotaxonomic groups [diatoms, dinoflagellates, cryptophytes, cyanobacteria (cyanophytes and prochlorophytes), and other eukaryotes (chlorophytes, haptophytes, and prasinophytes)] resulted in positive correlations of both the proportion and abundances, suggesting that the five taxonomic abundances were well-estimated using the MEX. Additionally, MEX observations denoted spatial and seasonal variations of phytoplankton assemblages, with high contributions from other eukaryotes in every area and season, cyanobacteria highly during the summer in surface Kuroshio and Japan Sea waters, and diatoms in the Oyashio and Oyashio–Kuroshio transition areas and the Okhotsk Sea. Furthermore, ratios of water-column-integrated chlorophyll-based abundances to those on the surface at the chemotaxonomy group level were differed among the areas and groups. These findings suggested that phytoplankton-assemblage monitoring in the vertical direction is essential for evaluation of their current biomass, and that the MEX promotes the acquisition of these observations.

Removing the Effects of Tropical Dynamics from North Pacific Climate Variability

Teleconnections from the Tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño-Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved -- including ENSO diversity in the Tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific -- have a variety of overlapping spatial and temporal scales. Here we use a multivariate Linear Inverse Model to quantify tropical and extra-tropical multi-scale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the Tropics are responsible for almost half of the seasonal variance, and almost three quarters of the decadal variance, along the North American coast and within the subtropical front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the Northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio-Oyashio Extension (KOE) region is considerably less impacted by the Tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific Decadal Oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the Tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.

Enhanced interactions of Kuroshio Extension with tropical Pacific in a changing climate

Quasi-decadal climate of the Kuroshio Extension (KE) is pivotal to understanding the North Pacific coupled ocean–atmosphere dynamics and their predictability. Recent observational studies suggest that extratropical-tropical coupling between the KE and the central tropical Pacific El Niño Southern Oscillation (CP-ENSO) leads to the observed preferred decadal time-scale of Pacific climate variability. By combining reanalysis data with numerical simulations from a high-resolution climate model and a linear inverse model (LIM), we confirm that KE and CP-ENSO dynamics are linked through extratropical-tropical teleconnections. Specifically, the atmospheric response to the KE excites Meridional Modes that energize the CP-ENSO (extratropicstropics), and in turn, CP-ENSO teleconnections energize the extratropical atmospheric forcing of the KE (tropicsextratropics). However, both observations and the model show that the KE/CP-ENSO coupling is non-stationary and has intensified in recent decades after the mid-1980. Given the short length of the observational and climate model record, it is difficult to attribute this shift to anthropogenic forcing. However, using a large-ensemble of the LIM we show that the intensification in the KE/CP-ENSO coupling after the mid-1980 is significant and linked to changes in the KE atmospheric downstream response, which exhibit a stronger imprint on the subtropical winds that excite the Pacific Meridional modes and CP-ENSO.

Subseasonal Predictability of the North Atlantic Oscillation

<div> <p>Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related Northern Annular mode (NAM), on “subseasonal” (weeks to a few months) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events. Unfortunately, the relatively fast, weather-related processes dominating overall NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally-based Linear Inverse Model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM (run operationally at NOAA's Climate Prediction Center) and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.</p> </div>

A Linear Inverse Model of Tropical and South Pacific Climate Variability: Optimal Structure and Stochastic Forcing

A stochastically forced linear inverse model (LIM) of the combined modes of variability from the tropical and South Pacific Oceans is used to investigate the linear growth of optimal initial perturbations and to identify the spatiotemporal features of the stochastic forcing associated with the atmospheric Pacific–South American patterns 1 and 2 (PSA1 and PSA2). Optimal initial perturbations are shown to project onto El Niño–Southern Oscillation (ENSO) and South Pacific decadal oscillation (SPDO), where the inclusion of subsurface South Pacific Ocean temperature variability significantly increases the multiyear linear predictability of the deterministic system. We show that the optimal extratropical sea surface temperature (SST) precursor is associated with the South Pacific meridional mode, which takes from 7 to 9 months to linearly evolve into the final ENSO and SPDO peaks in both the observations and as simulated in an atmosphere-forced ocean model. The optimal subsurface precursor resembles its peak phase, but with a weak amplitude, representing oceanic Rossby waves in the extratropical South Pacific. The stochastic forcing is estimated as the residual by removing the deterministic dynamics from the actual tendency under a centered difference approximation. The resulting stochastic forcing time series satisfies the Gaussian white noise assumption of the LIM. We show that the PSA-like variability is strongly associated with stochastic SST forcing in the tropical and South Pacific Oceans and contributes not only to excite the optimal initial perturbations associated with ENSO and the SPDO but in general to activate the entire stochastic SST forcing, especially in austral summer.

Impact of Annual Cycle on ENSO Variability and Predictability

Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.

Enhanced Interactions of Kuroshio Extension With Tropical Pacific in a Changing Climate

Quasi-decadal climate of the Kuroshio Extension (KE) is pivotal to understanding the North Pacific coupled ocean-atmosphere dynamics and their predictability. Recent observational studies suggest that extratropical-tropical coupling between the KE and the central tropical Pacific El Niño Southern Oscillation (CP-ENSO) leads to the observed preferred decadal time-scale of Pacific climate variability. By combining reanalysis data with numerical simulations from a high-resolution climate model and a linear inverse model (LIM), we confirm that KE and CP-ENSO dynamics are linked through extratropical-tropical teleconnections. Specifically, the atmospheric response to the KE excites Meridional Modes that energize the CP-ENSO (extratropics→tropics), and in turn, CP-ENSO teleconnections energize the extratropical atmospheric forcing of the KE (tropics→extratropics). However, both observations and the model show that the KE/CP-ENSO coupling is non-stationary and has intensified in recent decades after the mid-1980. Given the short length of the observational and climate model record, it is difficult to attribute this shift to anthropogenic forcing. However, using a large-ensemble of the LIM we show that the intensification in the KE/CP-ENSO coupling after the mid-1980 is significant and linked to changes in the KE atmospheric downstream response, which exhibit a stronger imprint on the subtropical winds that excite the Pacific Meridional modes and CP-ENSO.

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extratropical sea surface temperature, and South Pacific Ocean vertically averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble El Niño–Southern Oscillation (ENSO) and the South Pacific decadal oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating that the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the midlatitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the high-frequency atmospheric variability of the Pacific–South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in March–May (MAM) but displays a significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June–September (JAS), indicating remote but delayed influences from the tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability and further that by characterizing the upper ocean temperature contribution in the LIM, the seasonal predictability of both ENSO and the SPDO variability is increased.

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

<p>A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extra-tropical sea surface temperature, and South Pacific Ocean vertically-averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble the El-Niño Southern Oscillation (ENSO) and the South Pacific Decadal Oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the mid-latitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the atmospheric high-frequency variability of the Pacific South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in Mar-May (MAM) but nevertheless displays significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June-September (JAS), indicating remote but delayed influences from the Tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability, and further that by characterizing the upper ocean temperature contribution in the LIM the seasonal predictability of both ENSO and the SPDO variability is increased.</p>

Mapping Large-scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal Prediction

The excitation of the Pacific-North American (PNA) teleconnection pattern by the Madden-Julian Oscillation (MJO) has been considered as one of the most important predictability sources on subseasonal timescales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical-extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced which leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g. atmospheric rivers) on subseasonal timescales. Consistent with the findings of the first part, most of the predictable signals on subseasonal timescales are determined by the dynamics of MJO-PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.