The South China Sea Mooring Array and its applications in exploring oceanic multiscale dynamics

The South China Sea (SCS) is abundant with complex multiscale dynamic processes but their spatiotemporal variations, generation and evolution mechanisms, and mutual interactions remain inadequately understood due to the lack of long-term in situ observations. To explore oceanic multiscale dynamics in the SCS, the SCS Mooring Array (SCSMA) was began to be constructed since 2009. The SCSMA consists of ∼40 moorings and is the largest in situ ocean observing system in marginal seas worldwide. The highest spatial resolution of SCSMA is ∼1.5 km, and the longest duration of fixed-location observations reaches ∼15 years. Long-term observations from the SCSMA have allowed significant advances in the understanding of large-scale circulation, mesoscale eddies, submesoscale processes, internal waves, turbulent mixing, and interactions and energy cascades of different processes. In particular, the full chain of forward energy cascade from large-scale currents to turbulence has been revealed. Different dynamic processes in the SCS constitute a three-dimensional multiscale circulation system which transports materials and tracers in a relay. Here, we first describe the SCSMA and its construction process, and then comprehensively review its relevant advances in multiscale dynamics. Prospects for the SCSMA and its future applications are given at the end.

Simulated Sea Surface Salinity Data from a 1/48° Ocean Model

To study the validation process for sea surface salinity (SSS) we have generated one year (November 2011- October 2012) of simulated satellite and in situ "ground truth" data. This was done using the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° simulation, the highest resolution global ocean model currently available. The ground tracks of three satellites, Aquarius, SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were extracted and used to sample the model with a gaussian weighting similar to that of the spaceborne sensor ground footprint. This produced simulated level 2 (L2) data. Simulated level 3 (L3) data were then produced by averaging L2 data onto a regular grid. The model was sampled to produce simulated Argo and tropical mooring SSS datasets. The Argo data were combined into a simulated gridded monthly 1° Argo product. The simulated data produced from this effort have been used to study sampling errors, matchups, subfootprint variability and the validation process for SSS at L2 and L3.

From Stamps to Parabolas

I am a child of Sputnik, the satellite launched by the Soviet Union in 1957. That event created opportunities for me to escape the horrors of apartheid by emigrating from South Africa to the United States. There, fortuitously, I was given excellent opportunities to explore how an interplay between the waves and currents influences climate variability, from interannual El Niño events to millennial ice ages. During my career, I also witnessed intriguing facets of the interactions between the profoundly different worlds of science and of human affairs. Up to 1957, El Niño was welcomed as a blessing, but by 1982 it had become a curse-not because it changed, but because our human activities are making us vulnerable to natural climate variability. We have learned to cope admirably with the occasional failures of the Indian monsoons; the resultant famines are not as calamitous as they once were. What guidance does that limited success provide for a response to global warming, a climate change we humans are inducing? This article briefly summarizes how my career as a geoscientist brought me to the conclusion that a strategy to promote responsible stewardship of planet Earth should be based on love rather than fear. We can only love what we know, so warnings of imminent gloom and doom should be complemented with efforts to make everyone aware of the wonders of our amazing planet-the only one in the universe known to be habitable.

A global long-term ocean surface daily/0.05° net radiation product from 1983–2020

The all-wave net radiation (Rn) on the ocean surface characterizes the available radiative energy balance and is important to understand the Earth’s climate system. Considering the shortcomings of available ocean surface Rn datasets (e.g., coarse spatial resolutions, discrepancy in accuracy, inconsistency, and short duration), a new long-term global daily Rn product at a spatial resolution of 0.05° from 1983 to 2020, as part of the Global High Resolution Ocean Surface Energy (GHOSE) products suite, was generated in this study by fusing several existing datasets including satellite and reanalysis products based on the comprehensive in situ measurements from 68 globally distributed moored buoy sites. Evaluation against in-situ measurements shows the root mean square difference, mean bias error and correlation coefficient squared of 23.56 Wm−2, 0.88 Wm−2 and 0.878. The global average ocean surface Rn over 1983–2020 is estimated to be 119.71 ± 2.78 Wm−2 with a significant increasing rate of 0.16 Wm−2 per year. GHOSE Rn product can be valuable for oceanic and climatic studies.

Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches

In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.

Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation

Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a latent heat flux diagnostic that depicts how latent heat fluxes vary with the near-surface specific humidity vertical gradient (Δq) and surface wind speed (|V|). Compared to fluxes estimated from |V| and Δq measured at tropical moorings and the Coupled Ocean Atmosphere Response Experiment 3.0 (COARE3.0) algorithm, tropical latent heat fluxes in the National Center for Atmospheric Research CEMS2 and Department of Energy E3SMv1 models are significantly overestimated at |V| and Δq extrema. Madden-Julian oscillation (MJO) sensitivity to surface flux algorithm is tested with offline and inline flux corrections. The offline correction adjusts model output fluxes toward mooring-estimated fluxes; the inline correction replaces the original bulk flux algorithm with the COARE3.0 algorithm in atmosphere-only simulations of each model. Both corrections indicate reduced latent heat flux feedback to intraseasonal precipitation, in better agreement with observations, suggesting that model-simulated fluxes are overly supportive for maintaining MJO convection.

An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities

An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (∼1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent.

Enhanced jet stream waviness induced by suppressed tropical Pacific convection during boreal summer

Consensus on the cause of recent midlatitude circulation changes toward a wavier manner in the Northern Hemisphere has not been reached, albeit a number of studies collectively suggest that this phenomenon is driven by global warming and associated Arctic amplification. Here, through a fingerprint analysis of various global simulations and a tropical heating-imposed experiment, we suggest that the suppression of tropical convection along the Inter Tropical Convergence Zone induced by sea surface temperature (SST) cooling trends over the tropical Eastern Pacific contributed to the increased summertime midlatitude waviness in the past 40 years through the generation of a Rossby-wave-train propagating within the jet waveguide and the reduced north-south temperature gradient. This perspective indicates less of an influence from the Arctic amplification on the observed mid-latitude wave amplification than what was previously estimated. This study also emphasizes the need to better predict the tropical Pacific SST variability in order to project the summer jet waviness and consequent weather extremes.

The Northern Hemisphere summer circulation in the midlatitudes has become more “meandering” over the past decades, but the cause of the change remains elusive. Here the authors reveal that the waiver trending pattern results from internal climate forcing associated with sea surface temperature low frequency variability over the tropical Eastern Pacific.

A New Empirical Estimation Scheme for Daily Net Radiation at the Ocean Surface

Ocean surface net radiation (Rn) is significant in research on the Earth’s heat balance systems, air–sea interactions, and other applications. However, there have been few studies on Rn until now. Based on radiative and meteorological measurements collected from 66 globally distributed moored buoys, it was found that Rn was dominated by downward shortwave radiation (Rg↓) when the length ratio of daytime (LRD) was greater than 0.4 but dominated by downward longwave radiation (Rl↓) for the other cases (LRD ≤ 0.4). Therefore, an empirical scheme that includes two conditional models named Case 1 (LRD > 0.4) utilizing Rg↓ as a major input and Case 2 (LRD ≤ 0.4) utilizing Rl↓ as a major input for Rn estimation was successfully developed. After validation against in situ Rn, the performance of the empirical scheme was satisfactory with an overall R2 value of 0.972, an RMSE of 9.768 Wm−2, and a bias of −0.092 Wm−2. Specifically, the accuracies of the two conditional models were also very good, with RMSEs of 9.805 and 2.824 Wm−2 and biases of −0.095 and 0.346 Wm−2 for the Case 1 and Case 2 models, respectively. However, due to the limited number of available samples, the performances of these new models were poor in coastal and high-latitude areas, and the models did not work when the LRD was too small (i.e., LRD < 0.3). Overall, the newly developed empirical scheme for Rn estimation has strong potential to be widely used in practical use because of its simple format and high accuracy.

Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

A new satellite-based product containing daily sea surface net radiation () values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm−2 at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm−2 before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself.

Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface. Moreover, the rain and wind signals are very similar, which makes it difficult to distinguish wind from rain. The rain contamination can be mitigated for radiometers that operate simultaneously at C-band and X-band channels, such as WindSat, AMSR-E and AMSR2. The basic principle is to use combinations between C-band and X-band channels that are sensitive to wind speed but relatively insensitive to rain. Based on this principle, we have developed algorithms for retrieving wind speeds in rain from the WindSat and AMSR sensors. These algorithms are statistical regressions and are trained specifically under tropical cyclone conditions. We lay out the steps of the algorithm development, training, and testing. The major source for training the algorithm is provided by wind speeds from the SMAP L-band radiometer, which have been proven to provide reliable wind speeds in strong storms and are not affected by rain. We show that the WindSat and AMSR tropical cyclone wind algorithms perform well under precipitation where standard passive wind speed retrievals fail. We examine the possibility of extending the C/X-band tropical cyclone wind algorithm to X/K-band channels and discuss how it can be broadened from tropical cyclone conditions to global winds in rain retrievals.

ENSO feedback drives variations in dieback at a marginal mangrove site

Ocean-atmosphere climatic interactions, such as those resulting from El Niño Southern Oscillation (ENSO) are known to influence sea level, sea surface temperature, air temperature, and rainfall in the western Pacific region, through to the north-west Australian Ningaloo coast. Mangroves are ecologically important refuges for biodiversity and a rich store of blue carbon. Locations such as the study site (Mangrove Bay, a World Heritage Site within Ningaloo Marine Park and Cape Range National Park) are at the aridity range-limit which means trees are small in stature, forests small in area, and are potentially susceptible to climate variability such as ENSO that brings lower sea level and higher temperature. Here we explore the relationship between mangrove dieback, and canopy condition with climatic variables and the Southern Oscillation Index (SOI)-a measure of ENSO intensity, through remote sensing classification of Landsat satellite missions across a 29 year period at a north-west Australian site. We find that the SOI, and seasonal mean minimum temperature are strongly correlated to mangrove green canopy (as indicator of live canopy) area. This understanding of climate variations and mangrove temporal heterogeneity (patterns of abundance and condition) highlights the sensitivity and dynamics of this mangrove forest and recommends further research in other arid and semi-arid tropical regions at mangrove range-limits to ascertain the extent of this relationship.

Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

The unique mean seasonal cycle in the Indian Ocean anchors its various air-sea coupled modes across the basin

The interannual variability of the sea surface temperature (SST) in the Indian Ocean is complex and characterized by various air-sea coupled modes, which occur around El Niño/La Niña's peak phase (i.e. December-January-February, DJF). Indian Ocean Dipole Mode (IODM) develops over the tropical Indian Ocean and peaks in September-October-November (SON), while Ningaloo Niño, Subtropical Indian Ocean Dipole (SIOD) and Indian Ocean Basin Mode (IOBM) occur respectively over northwest off Australia, subtropical and tropical Indian Ocean, during boreal winter to spring. The apparent contrast between their divergent regionality and convergent seasonality around DJF triggers the present study to examine the interaction between the local mean monsoonal cycle and the anomalous forcing from El Niño/La Niña. The diagnosis confirms that the Indian Ocean's unique complexity, including the monsoonal circulation over the tropics and the trade wind over the subtropical southern Indian Ocean, plays the fundamental role in anchoring the various regional air-sea coupled modes across the basin. The SST anomalies can be readily explained by the wind-evaporation-SST (WES) mechanism, which works together with other more regional-dependent dynamic and thermodynamic mechanisms. This implies that El Niño/La Niña brings much predictability for the Indian Ocean variations.

Revealing ocean-scale biochemical structure with a deep-diving vertical profiling autonomous vehicle

Vast and diverse microbial communities exist within the ocean. To better understand the global influence of these microorganisms on Earth's climate, we developed a robot capable of sampling dissolved and particulate seawater biochemistry across ocean basins while still capturing the fine-scale biogeochemical processes therein. Carbon and other nutrients are acquired and released by marine microorganisms as they build and break down organic matter. The scale of the ocean makes these processes globally relevant and, at the same time, challenging to fully characterize. Microbial community composition and ocean biochemistry vary across multiple physical scales up to that of the ocean basins. Other autonomous underwater vehicles are optimized for moving continuously and, primarily, horizontally through the ocean. In contrast, Clio, the robot that we describe, is designed to efficiently and precisely move vertically through the ocean, drift laterally in a Lagrangian manner to better observe water masses, and integrate with research vessel operations to map large horizontal scales to a depth of 6000 meters. We present results that show how Clio conducts high-resolution sensor surveys and sample return missions, including a mapping of 1144 kilometers of the Sargasso Sea to a depth of 1000 meters. We further show how the samples obtain filtered biomass from seawater that enable genomic and proteomic measurements not possible through in situ sensing. These results demonstrate a robotic oceanography approach for global-scale surveys of ocean biochemistry.

High sensitivity of tropical precipitation to local sea surface temperature

Precipitation and atmospheric circulation are the coupled processes through which tropical ocean surface temperatures drive global weather and climate^1–5. Local ocean surface warming tends to increase precipitation, but this local control is hard to disentangle from remote effects of conditions elsewhere. Such remote effects occur, for example, from El Niño Southern Oscillation (ENSO) events in the equatorial Pacific, which alter precipitation across the tropics. Atmospheric circulations associated with tropical precipitation are predominantly deep, extending up to the tropopause. Shallow atmospheric circulations^6–8, impacting the lower troposphere, also occur, but the importance of their interaction with precipitation is unclear. Uncertainty in precipitation observations^9,10, and limited observations of shallow circulations¹¹, further obstruct understanding of the ocean’s influence on weather and climate. Despite decades of research, persistent biases remain in many numerical model simulations^12–18, including excessively-wide tropical rainbands^14,18, the ‘double-intertropical convergence zone (ITCZ) problem’^12,16,17 and too-weak responses to ENSO¹⁵. These demonstrate stubborn gaps in our understanding, reducing confidence in forecasts and projections. Here we show that the real world has a high sensitivity of seasonal tropical precipitation to local sea-surface temperature. Our best observational estimate is 80% precipitation change per g/kg change in the saturation specific humidity (itself a function of the ocean surface temperature). This observed sensitivity is higher than in 43 of the 47 climate models studied, and is associated with strong shallow circulations. Models with more realistic sensitivity have smaller biases across a wide range of metrics. Our results apply to both temporal and spatial variation, over regions where climatological precipitation is around 1 mm/day or greater. Novel analysis of multiple independent observations, physical constraints and model data, underpin these findings. The spread in model behaviour is further linked to differences in shallow convection, providing a focus for accelerated research, to improve seasonal forecasts through multidecadal climate projections.

On the interchangeability of sea-surface and near-surface air temperature anomalies in climatologies

On global and hemispheric scales, sea-surface temperature (SST) anomalies are assumed to be good surrogates for near-surface marine air temperature (MAT) anomalies. In fact, global gridded temperature datasets commonly blend SST and near-surface air temperature anomalies to overcome the lack of geographically homogeneous and reliable MAT observations. Here, we show that SST and MAT anomalies differ regarding crucial statistical properties such as multiannual trends and probabilistic distributions of daily and monthly averages. We provide evidence of the lack of interchangeability from an array of moored buoys in the tropical Pacific Ocean. We identify statistically significant discrepancies between SST and MAT anomalies for single as well as groups of such buoys. Thus, caution is required when characterizing and interpreting MAT variability through SST observations, especially at shorter than decadal timescale.

Closing the sea surface mixed layer temperature budget from in situ observations alone: Operation Advection during BoBBLE

Sea surface temperature (SST) is a fundamental driver of tropical weather systems such as monsoon rainfall and tropical cyclones. However, understanding of the factors that control SST variability is lacking, especially during the monsoons when in situ observations are sparse. Here we use a ground-breaking observational approach to determine the controls on the SST variability in the southern Bay of Bengal. We achieve this through the first full closure of the ocean mixed layer energy budget derived entirely from in situ observations during the Bay of Bengal Boundary Layer Experiment (BoBBLE). Locally measured horizontal advection and entrainment contribute more significantly than expected to SST evolution and thus oceanic variability during the observation period. These processes are poorly resolved by state-of-the-art climate models, which may contribute to poor representation of monsoon rainfall variability. The novel techniques presented here provide a blueprint for future observational experiments to quantify the mixed layer heat budget on longer time scales and to evaluate these processes in models.

CYGNSS Surface Heat Flux Product Development

Ocean surface heat fluxes play a significant role in the genesis and evolution of various marine-based atmospheric phenomena, from the synoptic scale down to the microscale. While in-situ measurements from buoys and flux towers will continue to be the standard in regard to surface heat flux estimates, they commonly have significant gaps in temporal and spatial coverage. Previous and current satellite missions have filled these gaps; though they may not observe the fluxes directly, they can measure the variables needed (wind speed, temperature and humidity) to estimate latent and sensible heat fluxes. However, current remote sensing instruments have their own limitations, such as infrequent coverage, signals attenuated by precipitation or both. The Cyclone Global Navigation Satellite System (CYGNSS) mission overcomes these limitations over the tropical and subtropical oceans by providing improved coverage in nearly all weather conditions. While CYGNSS (Level 2) primarily estimates surface winds, when coupled with observations or estimates of temperature and humidity from reanalysis data, it can provide estimates of latent and sensible heat fluxes along its orbit. This paper describes the development of the Surface Heat Flux Product for the CYGNSS mission, its current results and expected improvements and changes in future releases.

50 Years of Satellite Remote Sensing of the Ocean

The development of the technologies of remote sensing of the ocean was initiated in the 1970s, while the ideas of observing the ocean from space were conceived in the late 1960s. The first global view from space revealed the expanse and complexity of the state of the ocean that had perplexed and inspired oceanographers ever since. This paper presents a glimpse of the vast progress made from ocean remote sensing in the past 50 years that has a profound impact on the ways we study the ocean in relation to weather and climate. The new view from space in conjunction with the deployment of an unprecedented amount of in situ observations of the ocean has led to a revolution in physical oceanography. The highlights of the achievement include the description and understanding of the global ocean circulation, the air–sea fluxes driving the coupled ocean–atmosphere system that is most prominently illustrated in the tropical oceans. The polar oceans are most sensitive to climate change with significant consequences, but owing to remoteness they were not accessible until the space age. Fundamental discoveries have been made on the evolution of the state of sea ice as well as the circulation of the ice-covered ocean. Many surprises emerged from the extraordinary accuracy and expanse of the space observations. Notable examples include the determination of the global mean sea level rise as well as the role of the deep ocean in tidal mixing and dissipation.

Madden-Julian Oscillation Enhances Phytoplankton Biomass in the Maritime Continent

In addition to monsoon-driven rainfall, the Maritime Continent (MC) is subject to heavy precipitation caused by the Madden–Julian Oscillation (MJO), a tropical convection-coupled circulation that propagates eastward from the Indian to the Pacific Ocean. This study shows that riverine runoff from MJO-driven rainfall in the western MC significantly enhances phytoplankton biomass not only in the coastal regions but as far as the nutrient-poor Banda Sea, located 1,000 km downstream of the riverine source. We present observational estimates of the chlorophyll-a concentration in the Banda Sea increasing by 20% over the winter average within an MJO life cycle. The enhancement of phytoplankton in the central Banda Sea is attributed to two coinciding MJO-triggered mechanisms: enhanced sediment loading and eastward advection of waters with high sediment and chlorophyll concentrations. Our results highlight an unexpected effect of MJO-driven rainfall on the downstream oceanic region. This finding has significant implications for the marine food chain and biogeochemical processes in the MC, given the increasing deforestation rate and projections that global warming will intensify both the frequency and strength of MJO-driven rainfall in the MC.

El Niño–Southern Oscillation and its impact in the changing climate

Extensive research has improved our understanding and forecast of the occurrence, evolution and global impacts of the El Niño–Southern Oscillation (ENSO). However, ENSO changes as the global climate warms up and it exhibits different characteristics and climate impacts in the twenty-first century from the twentieth century. Climate models project that ENSO will also change in the warming future and have not reached an agreement about the flavor, as to the intensity and the frequency, of future ENSO conditions. This article presents the conventional view of ENSO properties, dynamics and teleconnections, and reviews the emerging understanding of the diversity and associated climate impacts of ENSO. It also reviews the results from investigations into the possible changes in ENSO under the future global-warming scenarios.

Influence of El Niño on atmospheric CO2 over the tropical Pacific Ocean: Findings from NASA's OCO-2 mission

Spaceborne observations of carbon dioxide (CO₂) from the Orbiting Carbon Observatory-2 are used to characterize the response of tropical atmospheric CO₂ concentrations to the strong El Niño event of 2015-2016. Although correlations between the growth rate of atmospheric CO₂ concentrations and the El Niño-Southern Oscillation are well known, the magnitude of the correlation and the timing of the responses of oceanic and terrestrial carbon cycle remain poorly constrained in space and time. We used space-based CO₂ observations to confirm that the tropical Pacific Ocean does play an early and important role in modulating the changes in atmospheric CO₂ concentrations during El Niño events-a phenomenon inferred but not previously observed because of insufficient high-density, broad-scale CO₂ observations over the tropics.

Evaluation of satellite and reanalysis-based global net surface energy flux and uncertainty estimates

The net surface energy flux is central to the climate system yet observational limitations lead to substantial uncertainty. A combination of satellite-derived radiative fluxes at the top of atmosphere adjusted using the latest estimation of the net heat uptake of the Earth system, and the atmospheric energy tendencies and transports from the ERA-Interim reanalysis are used to estimate surface energy flux globally. To consider snowmelt and improve regional realism, land surface fluxes are adjusted through a simple energy balance approach at each grid point. This energy adjustment is redistributed over the oceans to ensure energy conservation and maintain realistic global ocean heat uptake, using a weighting function to avoid meridional discontinuities. Calculated surface energy fluxes are evaluated through comparison to ocean reanalyses. Derived turbulent energy flux variability is compared with the Objectively Analyzed air-sea Fluxes (OAFLUX) product, and inferred meridional energy transports in the global ocean and the Atlantic are also evaluated using observations. Uncertainties in surface fluxes are investigated using a variety of approaches including comparison with a range of atmospheric reanalysis products. Decadal changes in the global mean and the interhemispheric energy imbalances are quantified, and present day cross-equator heat transports are reevaluated at 0.22 ± 0.15 PW (petawatts) southward by the atmosphere and 0.32 ± 0.16 PW northward by the ocean considering the observed ocean heat sinks.

Autonomous Multi-Platform Observations During the Salinity Processes in the Upper-ocean Regional Study

The Salinity Processes in the Upper-ocean Regional Study (SPURS) aims to understand the patterns and variability of sea surface salinity. In order to capture the wide range of spatial and temporal scales associated with processes controlling salinity in the upper ocean, research vessels delivered autonomous instruments to remote sites, one in the North Atlantic and one in the Eastern Pacific. Instruments sampled for one complete annual cycle at each of these two sites, which are subject to contrasting atmospheric forcing. The SPURS field programs coordinated sampling from many different platforms, using a mix of Lagrangian and Eulerian approaches. This article discusses the motivations, implementation, and first results of the SPURS-1 and SPURS-2 programs.

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

Assimilation methods, meant to constrain divergence of model trajectory from reality using observations, do not exactly satisfy the physical laws governing the model state variables. This allows mismatches in the analysis in the vicinity of observation locations where the effect of assimilation is most prominent. These mismatches are usually mitigated either by the model dynamics in between the analysis cycles and/or by assimilation at the next analysis cycle. However, if the observations coverage is limited in space, as it was in the ocean before the Argo era, these mechanisms may be insufficient to dampen the mismatches, which we call shocks, and they may remain and grow. Here we show through controlled experiments, using real and simulated observations in two different ocean models and assimilation systems, that such shocks are generated in the ocean at the lateral boundaries of the moored buoy network. They thrive and propagate westward as Rossby waves along these boundaries. However, these shocks are essentially eliminated by the assimilation of near-homogenous global Argo distribution. These findings question the fidelity of ocean reanalysis products in the pre-Argo era. For example, a reanalysis that ignores Argo floats and assimilates only moored buoys, wrongly represents 2008 as a negative Indian Ocean Dipole year.

Decadal-Scale Forecasting of Climate Drivers for Marine Applications

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events-a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors-fisheries, aquaculture, tourism, biodiversity management, infrastructure-is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales.

Mitigation of Coral Reef Warming Across the Central Pacific by the Equatorial Undercurrent: A Past and Future Divide

Global climate models (GCMs) predict enhanced warming and nutrient decline across the central tropical Pacific as trade winds weaken with global warming. Concurrent changes in circulation, however, have potential to mitigate these effects for equatorial islands. The implications for densely populated island nations, whose livelihoods depend on ecosystem services, are significant. A unique suite of in situ measurements coupled with state-of-the-art GCM simulations enables us to quantify the mitigation potential of the projected circulation change for three coral reef ecosystems under two future scenarios. Estimated historical trends indicate that over 100% of the large-scale warming to date has been offset locally by changes in circulation, while future simulations predict a warming mitigation effect of only 5–10% depending on the island. The pace and extent to which GCM projections overwhelm historical trends will play a key role in defining the fate of marine ecosystems and island communities across the tropical Pacific.

The impact of El Niño events on the pelagic food chain in the northern California Current

The zooplankton of the northern California Current are typically characterized by an abundance of lipid-rich copepods that support rapid growth and survival of ecologically, commercially, and recreationally valued fish, birds, and mammals. Disruption of this food chain and reduced ecosystem productivity are often associated with climatic variability such as El Niño events. We examined the variability in timing, magnitude, and duration of positive temperature anomalies and changes in copepod species composition in the northern California Current in relation to 10 tropical El Niño events. Measurable impacts on mesozooplankton of the northern California Current were observed during seven of 10 of these events. The occurrence of anomalously warm water and the response of the copepod community was rapid (lag of zero to 2 months) following the initiation of canonical Eastern Pacific (EP) events, but delayed (lag of 2-8 months) following 'Modoki' Central Pacific (CP) events. The variable lags in the timing of a physical and biological response led to impacts in the northern California Current peaking in winter during EP events and in the spring during CP events. The magnitude and duration of the temperature and copepod anomalies were strongly and positively related to the magnitude and duration of El Niño events, but were also sensitive to the phase of the lower frequency Pacific Decadal Oscillation. When fisheries managers and biological oceanographers are faced with the prospect of a future El Niño event, prudent management and observation will require consideration of the background oceanographic conditions, the type of event, and both the magnitude and duration of the event when assessing the potential physical and biological impacts on the northern California Current.

A new Approach to El Niño Prediction beyond the Spring Season

The enormous societal importance of accurate El Niño forecasts has long been recognized. Nonetheless, our predictive capabilities were once more shown to be inadequate in 2014 when an El Nino event was widely predicted by international climate centers but failed to materialize. This result highlighted the problem of the opaque spring persistence barrier, which severely restricts longer-term, accurate forecasting beyond boreal spring. Here we show that the role played by tropical seasonality in the evolution of the El Niño is changing on pentadal (five-year) to decadal timescales and thus that El Niño predictions beyond boreal spring will inevitably be uncertain if this change is neglected. To address this problem, our new coupled climate simulation incorporates these long-term influences directly and generates accurate hindcasts for the 7 major historical El Niños. The error value between predicted and observed sea surface temperature (SST) in a specific tropical region (5°N–5°S and 170°–120°W) can consequently be reduced by 0.6 Kelvin for one-year predictions. This correction is substantial since an “El Niño” is confirmed when the SST anomaly becomes greater than +0.5 Kelvin. Our 2014 forecast is in line with the observed development of the tropical climate.

Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting

In this paper, we emphasize the importance of accurate initial conditions in predicting high-impact ocean-atmospheric environmental events, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), tropical cyclone (TC), and Kuroshio large meander (KLM), by reviewing recent progresses toward target observations for improving the initialization of these events forecasting. Since field observations are costly and will never be dense enough to fully cover the vast space of these events, it is necessary to develop methodologies that guide the design of efficient and effective observation strategy. Of particular interest is a method called conditional non-linear optimal perturbation (CNOP), which has been shown to be very useful in determining the sensitive areas for target observations applicable to the predictions of ENSO, IOD, TC, and KLM. Further studies are needed to understand the predictability of these events under the influence of climate change, and to explore the possibility of implementing field programs of target observations. These studies are challenging but are crucially important for improving our forecast skill of the high-impact ocean-atmospheric environmental events, and thus for disaster prevention, climate change mitigation, and sustainable socio-economic development.

Using transfer functions to quantify El Niño Southern Oscillation dynamics in data and models

Transfer function tools commonly used in engineering control analysis can be used to better understand the dynamics of El Niño Southern Oscillation (ENSO), compare data with models and identify systematic model errors. The transfer function describes the frequency-dependent input-output relationship between any pair of causally related variables, and can be estimated from time series. This can be used first to assess whether the underlying relationship is or is not frequency dependent, and if so, to diagnose the underlying differential equations that relate the variables, and hence describe the dynamics of individual subsystem processes relevant to ENSO. Estimating process parameters allows the identification of compensating model errors that may lead to a seemingly realistic simulation in spite of incorrect model physics. This tool is applied here to the TAO array ocean data, the GFDL-CM2.1 and CCSM4 general circulation models, and to the Cane-Zebiak ENSO model. The delayed oscillator description is used to motivate a few relevant processes involved in the dynamics, although any other ENSO mechanism could be used instead. We identify several differences in the processes between the models and data that may be useful for model improvement. The transfer function methodology is also useful in understanding the dynamics and evaluating models of other climate processes.

An unforgettable event: a qualitative study of the 1997-98 El Niño in northern Peru

During the 1997-98 El Niño, Tumbes, Peru received 16 times the annual average rainfall. This study explores how Tumbes residents perceived the impact of the El Niño event on basic necessities, transport, health care, jobs and migration. Forty-five individuals from five rural communities, some of which were isolated from the rest of Tumbes during the event, participated in five focus groups; six of these individuals constructed nutrition diaries. When asked about events in the past 20 years, participants identified the 1997-98 El Niño as a major negative event. The El Niño disaster situation induced a decrease in access to transport and health care and the rise in infectious diseases was swiftly contained. Residents needed more time to rebuild housing; recover agriculture, livestock and income stability; and return to eating sufficient animal protein. Although large-scale assistance minimized effects of the disaster, residents needed more support. Residents' perspectives on their risk of flooding should be considered in generating effective assistance policies and programmes.

Seasonal sea surface cooling in the equatorial Pacific cold tongue controlled by ocean mixing

Sea surface temperature (SST) is a critical control on the atmosphere, and numerical models of atmosphere-ocean circulation emphasize its accurate prediction. Yet many models demonstrate large, systematic biases in simulated SST in the equatorial 'cold tongues' (expansive regions of net heat uptake from the atmosphere) of the Atlantic and Pacific oceans, particularly with regard to a central but little-understood feature of tropical oceans: a strong seasonal cycle. The biases may be related to the inability of models to constrain turbulent mixing realistically, given that turbulent mixing, combined with seasonal variations in atmospheric heating, determines SST. In temperate oceans, the seasonal SST cycle is clearly related to varying solar heating; in the tropics, however, SSTs vary seasonally in the absence of similar variations in solar inputs. Turbulent mixing has long been a likely explanation, but firm, long-term observational evidence has been absent. Here we show the existence of a distinctive seasonal cycle of subsurface cooling via mixing in the equatorial Pacific cold tongue, using multi-year measurements of turbulence in the ocean. In boreal spring, SST rises by 2 kelvin when heating of the upper ocean by the atmosphere exceeds cooling by mixing from below. In boreal summer, SST decreases because cooling from below exceeds heating from above. When the effects of lateral advection are considered, the magnitude of summer cooling via mixing (4 kelvin per month) is equivalent to that required to counter the heating terms. These results provide quantitative assessment of how mixing varies on timescales longer than a few weeks, clearly showing its controlling influence on seasonal cooling of SST in a critical oceanic regime.