Global Tropical Moored Buoy Array Research Articles

Ambiguous Variations in Tropical Latent Heat Flux since the Years around 1998

Abstract The tropical latent heat flux (LHF) has experienced a significant increase under the background of global warming in the past four decades. However, since the years around 1998, the long-term LHF variations in the tropics have been found to be quite different in various flux products. Three different trends in the LHF, climbing, near zero, and declining, are suggested by five widely used flux products, which hinders our knowledge of the actual LHF variations. Although there are buoy observations in the tropics, these observations are hard to use to evaluate flux products as they have been assimilated and/or used as benchmarks in the flux data production. This study aims to identify credible long-term LHF variations since 1998. A linear model decomposing the LHF variations into contributions from sea surface wind U and air–sea humidity differences is first applied. The linear model results show that the LHF variations have been more positively connected to U variations since 1998. Evidence from in situ and remote sensing observations is subsequently employed to identify how U has varied recently. Both Global Tropical Moored Buoy Array (GTMBA) buoy observations (from 82 buoys) and a multisensor merged satellite product support a slightly downward trend in U in the last two decades. Such a weakening of U is not conducive to oceanic evaporation and leads to a reduced LHF. Consequently, a declining LHF under a weakening U since the emergence of the global warming “hiatus” in approximately 1998 might be more convincing in the sense of data accuracy and physical consistency. Significance Statement The latent heat flux acts as the language of air–sea interactions. This study aims to examine how the tropical latent heat flux has changed since the emergence of the global warming slowdown in approximately 1998. The most striking finding is that the long-term variations in the tropical latent heat flux are fairly inconsistent in several widely used flux products in the last two decades. The sea surface wind variation is found to be the primary contributor to the latent heat flux variation after 1998. Observational evidence from buoy and remote sensing data is hence employed to clarify the actual sea surface wind and the latent heat flux variations.

Key Role of Subdaily Wind Variability for Tropical Surface Wind Stress

Abstract High-frequency observations of surface winds over the open ocean are available only at limited locations. However, these observations are essential for assessing atmospheric influences on the ocean, validating reanalysis products, and building parameterization schemes. By analyzing high-frequency measurements from the Global Tropical Moored Buoy Array, the effects of subdaily winds on the mean surface wind stress magnitude are systematically examined. Subdaily winds account for 12.4% of the total stress magnitude on average. The contribution is enhanced over the intertropical convergence zone and reaches a maximum (28.5%) in the equatorial western Pacific. The magnitude of the contribution is primarily determined by the kinetic energy of subdaily winds. Compared to the buoy observations, the ERA5 and MERRA2 subdaily winds underestimate this contribution by 51% and 63% due to underestimations of subdaily kinetic energy, leading to 7% and 8% underestimations in the total stress magnitude, respectively. Two new gustiness parameterization schemes related to precipitation are developed to account for the effect of subdaily winds, explaining ∼80% of the contribution from subdaily winds. Considering the importance of wind stress for ocean–atmosphere interactions, the inclusion of these parameterization schemes in climate models is expected to substantially improve simulations of large-scale climate variability. Significance Statement Surface wind stress drives upper-ocean circulation, which is critical for the redistribution of mass, momentum, and energy in the ocean. Moreover, it is one of the key factors controlling oceanic turbulent mixing and therefore has significant impacts on the distribution of temperature, salinity, and associated ocean variability. Using high-resolution buoy observations, this study highlights the importance of subdaily winds for integrated wind stress estimates. In addition, it finds that current state-of-the-art and widely used reanalysis products largely underestimate the effect of subdaily winds. Two new parameterization schemes are developed, leading to a better representation of the effect of subdaily winds. Including the proposed parameterization schemes in climate models is expected to substantially improve their simulations of large-scale climate variability.

Tropical Ocean Observations for Weather and Climate: A Decadal Overview of the Global Tropical Moored Buoy Array

This paper describes the evolution of the Global Tropical Moored Buoy Array (GTMBA) over the past decade since the last comprehensive and coordinated overview of the Pacific-Atlantic-Indian Ocean system in 2010. GTMBA provides sustained and systematic observations in real time for weather and climate research, forecasting, and assessments. It is maintained through multi-national consortia that support the Tropical Atmosphere Ocean (TAO) Array and the Triangle Trans-Ocean Buoy Network (TRITON) in the Pacific, the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), and the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean. Phenomena of interest span a wide range of weather and climate timescales, including tropical cyclones, the Madden-Julian Oscillation, the seasonal cycle, monsoon circulations, El Niño-Southern Oscillation, climate variations on decadal timescales, and trends related to climate change. Recent scientific advances enabled by GTMBA are reviewed along with array design changes that respond to new scientific imperatives and operational exigencies, and future directions are discussed.

Quantifying Uncertainties in CERES/MODIS Downwelling Radiation Fluxes in the Global Tropical Oceans

The Clouds and the Earth's Radiant Energy System program, which uses the Moderate Resolution Imaging Spectroradiometer (CM), has been updated with the launch of new satellites and the availability of newly upgraded radiation data. The spatial and temporal variability of daily averaged synoptic 1-degree CM version 3 (CMv3) (old) and version 4 (CMv4) (new) downwelling shortwave (Q S ) and longwave radiation (Q L ) data in the global tropical oceans spanning 30°S–30°N from 2000 to 2017 is investigated. Daily in situ data from the Global Tropical Moored Buoy Array were used to validate the CM data from 2000 to 2015. When compared to CMv3, both Q S and Q L in CMv4 show significant improvements in bias, root-mean-square error, and standard deviations. Furthermore, a long-term trend analysis shows that Q S has been increasing by 1 W m −2 per year in the Southern Hemisphere. In contrast, the Northern Hemisphere has a −0.7 W m −2 annual decreasing trend. Q S and Q L exhibit similar spatial trend patterns. However, in the Indian Ocean, Indo-Pacific warm pool region, and Southern Hemisphere, Q L spatial patterns in CMv3 and CMv4 differ with an opposite trend (0.5 W m −2 ). These annual trends in Q S and Q L could cause the sea surface temperature to change by −0.2 to 0.3 °C per year in the tropical oceans. These results stress the importance of accurate radiative flux data, and CMv4 can be an alternative to reanalysis or other model-simulated data.

Surviving Piracy and the Coronavirus Pandemic

As with other elements of the Global Ocean Observing System (GOOS), building and sustaining the Global Tropical Moored Buoy Array (GTMBA) has had its own set of challenges. Moored buoys are fish aggregation devices, so they attract fishermen who often either intentionally or inadvertently damage the moorings. Fishing vandalism is the greatest source of data and equipment loss in the array, and it has no easy solution. Deploying and recovering deep ocean moorings requires specially equipped research vessels with the necessary size, endurance, and speed to carry out operations safely and efficiently. Such ships are in high demand and in short supply. Likewise, maintaining adequate funding levels to support a network of 100 or more moorings in the global tropics has always been difficult. The funding and ship time challenges have been mitigated in part by the formation of multi-national partnerships that underpin the success of the overall venture. Two challenges unique to the past decade, however, have been piracy and the coronavirus (COVID-19) pandemic.

Derivation of New Daily Rainfall Values from TAO 1-Min Rain Gauge Data

Abstract Daily rainfall accumulation estimates have been derived from 1-min volume data collected via self-syphon rain gauges deployed in the Tropical Atmosphere–Ocean (TAO) array of oceanographic buoys. The underlying high-resolution volume data were obtained directly from the Global Tropical Moored Buoy Array (GTMBA) Project Office of NOAA/Pacific Marine Environmental Laboratory. The derived accumulations have been incorporated into the Pacific Rainfall (PACRAIN) database as estimated daily values to augment existing sea level oceanic rainfall records gathered using traditional rain gauges. They have also been included in the PACRAIN historical, monthly gridded rainfall product. The methodology presented, which employs differencing of least squares–regressed sensor levels about 0000 UTC and rain gauge syphon events, is shown to offer improved error characteristics over the methodology used to compute previously published GTMBA rain rates. In particular, the PACRAIN method yields larger coefficients of determination and smaller standard errors than the duplicated GTMBA method when applied to synthetic rainfall data with noise magnitude and decorrelation times encompassing those observed in the real 1-min data. These results are shown to be consistent with mathematical expectations. Sources of instrument and catchment errors, as well as evaporation, are discussed in the context of their potential effects on accumulation estimates for periods of a day or longer. Significance Statement In this paper, we describe the derivation of daily rainfall amounts from raw rain gauge data obtained from buoy-mounted rain gauges. These new accumulation estimates expand the store of rainfall estimates from locations approximating the open-ocean conditions of the tropical Pacific Ocean. The derivation technique we describe exhibits better performance than the method used to generate previously published estimates using the same dataset.

Comparison of SCATSAT-1 swath data with global in situ buoy winds

Indian Space Research Organisation (ISRO) launched SCATSAT-1 scatterometer, on 26 September 2016 as a continuation and enhancement of OSCAT onboard OceanSat-2. The present work carries out a detailed inter-comparison of swath-wise level 2B data from SCATSAT-1 with in situ winds from three moored buoy arrays: Global Tropical Moored Buoy Array (GTMBA), National Data Buoy Center (NDBC) and Ocean Moored Buoy Network for northern Indian Ocean (OMNI). To assess the agreement between the satellite and in situ observations in different regions, the comparison was split between tropical, extra-tropical and coastal regions for each of the global ocean basins. Furthermore, the agreement between the two wind vector datasets was also estimated at each buoy location separately. Poor circular correlations in wind direction (WD) were observed in regions of shallow bathymetry and low mean wind speed, while some WD errors were found to be associated with the failure to resolve directional ambiguity.

Importance of High‐Frequency (≤30‐day) Wind Variability to the Annual Climatology of the Surface Latent Heat Flux Inferred From the Global Tropical Moored Buoy Array

AbstractPrevious studies conducted based on reanalysis data sets have suggested that high‐frequency (≤30‐day) wind variability has a significant impact on the climatology of surface latent heat flux (LHF). The present study quantifies the impact of high‐frequency winds using field measurements derived from the global tropical moored buoy array (GTMBA) and, in turn, evaluates the performance of existing reanalysis products on capturing the role of these high‐frequency winds. The buoy observations demonstrate that high‐frequency winds at the n‐day (n = 1, 2, …, 30) timescale make a positive contribution to the annual climatology of LHF by nonlinearly enhancing the mean surface wind speed. Furthermore, this contribution increases as the timescale decreases, with maxima occurring at the 1‐day (subdaily) timescale. The subdaily winds contribute 4.6 W m−2, on average over all GTMBA sites. The largest contribution exceeds 10.0 W m−2 over the equatorial western Pacific Ocean. Hourly reanalysis products underestimate the contribution of the high‐frequency winds at timescales shorter than about a week, especially at the subdaily timescale, which is underestimated by approximately 50–60%. This finding suggests that an improvement in the performance of subdaily wind variability is needed to improve the fidelity of next‐generation reanalysis products.

Sea surface salinity short-term variability in the tropics

Abstract. Using data from the Global Tropical Moored Buoy Array, we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on timescales of 2–17 d. It is a proxy for subfootprint variability over a 100 km footprint as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than during non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, although tending to have smaller values.

Diurnal variation in surface latent heat flux and the effect of diurnal variability on the climatological latent heat flux over the tropical oceans

AbstractDiurnal variation in surface latent heat flux (LHF) and the effects of diurnal variations in LHF-related variables on the climatological LHF are examined using observations from the Global Tropical Moored Buoy Array. The estimated amplitude of the climatological diurnal LHF over the Indo-Pacific warm pool and the equatorial Pacific and Atlantic cold tongues is remarkable, with maximum values exceeding 20.0 W m−2. Diurnal variability of sea surface skin temperature (SSTskin) is the primary contributor to the diurnal LHF amplitude. Because the diurnal SSTskin amplitude has an inverse relationship with surface wind speed over the tropical oceans, an inverse spatial pattern between the diurnal LHF amplitude and surface wind speed results. Resolving diurnal variations in the SSTskin and wind improves the estimate of the climatological LHF by properly capturing the daytime SSTskin and daily mean wind speed, respectively. The diurnal SSTskin-associated contribution is large over the warm pool and equatorial cold tongues where low wind speeds tend to cause strong diurnal SSTskin warming, while the magnitude associated with the diurnal winds is large over the highly dynamic environment of the Inter-Tropical Convergence Zone. The total diurnal contribution is about 9.0 W m−2on average over the buoy sites. There appears to be a power function (linear) relationship between the diurnal SSTskin-associated (wind-associated) contribution and surface mean wind speed (wind speed enhancement from diurnal variability). The total contribution from diurnal variability can be estimated accurately from high-frequency surface wind measurements using these relationships.

Rectification of the Intraseasonal SST Variability by the Diurnal Cycle of SST Revealed by the Global Tropical Moored Buoy Array

AbstractIt has been suggested that the intraseasonal sea surface temperature (SST) variability in the tropical oceans can be amplified by the diurnal cycle of SST (dSST). Here, by analyzing the global tropical moored buoy array for the first time, we find that the intraseasonal SST variability is indeed amplified by the dSST in most of the tropical oceans, especially in the Indo‐Pacific warm pool, but weakened in the equatorial cold tongues of the Pacific and Atlantic Oceans. Such a divergent response is associated with the difference in atmosphere‐ocean interaction processes over these two regions. In the warm pool region, SST responds to the intraseasonal atmospheric variability, resulting in in‐phase intraseasonal fluctuations between SST and dSST that amplify the intraseasonal SST variability. However, in the cold tongue region, SST drives the atmospheric changes, which leads to out‐of‐phase intraseasonal fluctuations between SST and dSST and thus the inhibition of the intraseasonal SST variability.

Does ERA5 outperform satellite products in estimating atmospheric downward longwave radiation at the surface?

Atmospheric downward longwave radiation (DLR) is a key component of the surface energy budget in the Earth system. Satellite retrievals and atmospheric reanalysis estimates are the two typical approaches to obtaining a spatio-temporally continuous DLR product. In this study, we evaluated the DLR product from the latest ERA5 atmospheric reanalysis and the well-known Clouds and Earth's Radiant Energy System (CERES) satellite retrievals, against high-quality observations collected at 46 Baseline Surface Radiation Network (BSRN) stations over land surfaces and at 9 Global Tropical Moored Buoy Array (GTMBA) buoy stations. The accuracy of the ERA5 DLR product over land was found to be higher on average than that of CERES at hourly to monthly time scales. Conversely, ERA5 performed slightly worse than CERES-SYN when estimating DLR over the ocean surface. This is the first time that atmospheric reanalysis has performed better than satellite retrievals in estimating DLR over the land surface, demonstrating the potentially extensive application prospects for ERA5 as well as setting new challenges for quantitative remote sensing research.

How Accurate Are Satellite-Derived Surface Solar Radiation Products over Tropical Oceans?

AbstractSurface solar radiation (SSR) over the ocean is essential for studies of ocean–atmosphere interactions and marine ecology, and satellite remote sensing is a major way to obtain the SSR over ocean. A new high-resolution (10 km; 3 h) SSR product has recently been developed, mainly based on the newly released cloud product of the International Satellite Cloud Climatology Project H series (ISCCP-HXG), and is available for the period from July 1983 to December 2018. In this study, we compared this SSR product with in situ observations from 70 buoy sites in the Global Tropical Moored Buoy Array (GTMBA) and also compared it with another well-known satellite-derived SSR product from the Clouds and the Earth’s Radiant Energy System (CERES; edition 4.1), which has a spatial resolution of approximately 100 km. The results show that the ISCCP-HXG SSR product is generally more accurate than the CERES SSR product for both ocean and land surfaces. We also found that the accuracy of both satellite-derived SSR products (ISCCP-HXG and CRERS) was higher over ocean than over land and that the accuracy of ISCCP-HXG SSR improves greatly when the spatial resolution of the product is coarsened to ≥ 30 km.

Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations

Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement.

Evaluation of surface shortwave and longwave downwelling radiations over the global tropical oceans

In the present study, daily downwelling shortwave (QS) and longwave radiation (QL) data from one satellite and two hybrid products have been evaluated using Global Tropical Moored Buoy Array during 2001–2009 in the tropical oceans. Daily satellite data are used from the Clouds and Earth’s Radiant Energy System (CERES) program. Data are obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) (CM) aboard the Terra and Aqua satellites. Coordinated Ocean Research Experiments (CORE-II) and Tropical Flux data (TropFlux) are the other two hybrid products used in this study. The analysis shows that majority of QS observations as well as derived products lie in 200–300 Wm−2 range in all the three tropical oceans. Both QS and QL in all products overestimated the majority of the observations. Yet, they underestimated the lower (0–100 Wm−2) values in QS and higher (300–440 Wm−2) values in QL. Majority of the QL observations lie within 390–420 Wm−2 range, and CM slightly overestimated this observed distribution in the Pacific and the Atlantic Oceans. But, majority of the observations in the Indian Ocean lie within 420–450 Wm−2 range. This implies that the tropical Indian Ocean receives 30 Wm−2 more energy as compared to the tropical Pacific and the Atlantic in the form of downwelling longwave radiation. Daily observed QS shows dominant seasonal cycle over the central, the eastern Pacific and the eastern Atlantic. On the other hand, the western Pacific, the central Atlantic and the Indian Oceans show intraseasonal variations. All products show this variation with high root-mean-square error (RMSE) values (QS and QL) over the Indian Ocean than in the Pacific and the Atlantic Oceans. Downwelling radiation from CORE-II shows highest RMSE (for both QS and QL) with least correlation coefficient (CC), and TropFlux has lowest RMSE and highest CC among all products in all three tropical oceans. CM has intermediate values of standard deviation, CC and RMSE. These results are not seasonally dependent, since the seasonal statistics are consistent with seasonal changes. Assuming that the SST is only driven by the downwelling shortwave and longwave fluxes, the errors associated with monthly SST can be as large as 0.2–0.3 (0.1–0.2) °C associated with errors in QS (QL). Both QS and QL in CORE-II have lower spatial variability as compared to other datasets. QL in the tropical oceans shows seasonal spatial variability determined by intertropical convergence zone positions. This variability does not change significantly over the Pacific and the Atlantic Oceans. The summer and winter monsoon patterns in the Indian Ocean guide the QL variability. Opposite to QS, higher QL values have lower variability. Thus, this study aims at finding better radiation dataset to use in the numerical models and deduce that satellite data could be an alternative to existing reanalysis products.

Ocean Climate Monitoring

Measuring ocean physics and atmospheric conditions at the sea-surface has been taking place for decades in our world’s oceans. Enhancing RD we discuss a “Hybrid” air-sea interaction deep-sea monitoring system that has been developed in the private sector to mirror ocean-climate community data streams and has been successfully deployed on three basin-scaled programs in the Indian Ocean (RAMA, First Institute of Oceanography, FIO, China), the Andaman Sea (MOMSEI, Monsoon Onset Monitoring, FIO) and the Pacific Ocean (China’s Institute of Oceanology, Academy of Sciences research in the western tropical Pacific). This application is a base to build upon as new sensors are developed and increased sampling at higher resolutions is required. A multidisciplinary tool to add new biological and biogeochemical sensors to study interaction with the physical processes of our oceans. This application can also be used at FLUX sites to enhance the Argo Program, telemetry docking stations for autonomous vehicles such as sail-drones, gliders and wave riders for enhancement and contribution to the Global Tropical Moored Buoy Array (GTMBA), Global Ocean Observing System (GOOS), Global Climate Observing System (GCOS), and the Global Earth Observing System of Systems (GEOS).

Error Characterization of Sea Surface Salinity Products Using Triple Collocation Analysis

The triple collocation (TC) technique allows the simultaneous calibration of three independent, collocated data sources, while providing an estimate of their accuracy. In this paper, the TC is adapted to validate different salinity data products along the tropical band. The representativeness error (the true variance resolved by the relatively high-resolution systems but not by the relatively low-resolution system) is accounted for in the validation process. A method based on the intercalibration capabilities of TC is used to estimate the representativeness error for each triplet, which is found to impact between 15% and 50% the error estimation of the different products. The method also sorts the different products in terms of their resolving spatiotemporal scales. Six salinity products (sorted from smaller to larger scales) used were: the in situ data from the Global Tropical Moored Buoy Array (TAO), the GLORYS2V3 ocean reanalysis output provided by Copernicus, the satellite-derived Aquarius Level 3 version 4 (AV4) and Soil Moisture and Ocean Salinity (SMOS) objectively analyzed (SOA) maps, and the climatology maps provided by the World Ocean Atlas (WOA). This calibration study is limited to the year 2013, a year when all the products were available. This validation approach aims to assess the quality of the different salinity products at the satellite-resolved spatiotemporal scales. The results show that, at the AV4 resolved scales, the Aquarius product has an error of 0.17, and outperforms TAO, GLORYS2V3, and the SOA maps. However, at the SOA resolved scales (which are coarser than those of the Aquarius product because of the large OA correlation radii used), the SMOS product has an error of 0.20, slightly lower than that of GLORYS2V3, Aquarius, and TAO. The WOA products show the highest errors. Higher order calibration may lead to a more accurate assessment of the quality of the climatological products.

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5–10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.

Fulfilling Observing System Implementation Requirements with the Global Drifter Array

AbstractThe Global Ocean Observing System (GOOS) requirements for in situ surface temperature and velocity measurements call for observations at 5° × 5° resolution. A key component of the GOOS that measures these essential climate variables is the global array of surface drifters. In this study, statistical observing system sampling experiments are performed to evaluate how many drifters are required to achieve the GOOS requirements, both with and without the presence of a completed global tropical moored buoy array at 5°S–5°N. The statistics for these simulations are derived from the evolution of the actual global drifter array. It is concluded that drifters should be deployed within the near-equatorial band even though that band is also in principle covered by the tropical moored array, as the benefits of not doing so are marginal. It is also concluded that an optimal design half-life for the drifters is ~450 days, neglecting external sources of death, such as running aground or being picked up. Finally, it is concluded that comparing the drifter array size to the number of static 5° × 5° open-ocean bins is not an ideal performance indicator for system evaluation; a better performance indicator is the fraction of 5° × 5° open-ocean bins sampled, neglecting bins with high drifter death rates.

Assessing equatorial surface currents in the FOAM Global and Indian Ocean models against observations from the global tropical moored buoy array

Surface currents from 2007–2008 hindcasts of the Forecast Ocean Assimilation Model (FOAM) Global and Indian Ocean models are assessed against observations at 46 global tropical moored buoy array sites. Zonal (u) currents are less challenging to model than meridional flows (v) due to their lower frequency variability. The assimilative global model has reasonable skill for zonal currents but less skill for meridional currents. The assimilative models have higher skill than the corresponding non-assimilative models. A too-strong westward bias of the order of 20cm/s is evident along the equator in all model versionsused in this study. No extra skill is evident in the high resolution (1/12°) regional model compared to the coarser resolution (1/4°) global model.