Abstract

Diapycnal and diathermal diffusivity values in the upper thermocline are estimated from buoyancy and heat budgets for water volumes bounded by isopycnals and isotherms, the air–sea interface, and coastline where applicable. Comprehensive analysis is given to the Indian Ocean, with an extended global general description.

The Indian Ocean gains buoyancy in the north (especially in the northeast) and loses buoyancy in the subtropical south. Freshest and least-dense water appears in the Bay of Bengal and isopycnals outcrop southwestward from there and then southward. Computation of diapycnal diffusivity (K _ρ ) starts from the Bay of Bengal, expanding southwestward and southward and with depth. As isopycnals extend equatorward from the northeast and with increasing depth, K _ρ remains at about 1.3 cm² s⁻¹ for 20.2 σ _θ (Bay of Bengal) to 22.0 σ _θ (northeast Indian Ocean). Farther south (poleward) and at greater depth, K _ρ decreases from 0.9 cm² s⁻¹ for 23.0 σ _θ (north of 20°S) to 0.5 cm² s⁻¹ for 25.0 σ _θ (north of 35°S). Isotherms outcrop poleward from the equator. Diathermal diffusivity values computed from the heat budget are large at the equator and near the surface (4.0 cm² s⁻¹ for 28.5°C isotherm) but decrease rapidly poleward and with depth (1.3 cm² s⁻¹ for 27.0°C). This indicates stronger mixing either near the equator or the surface, or a possible component in the diathermal direction of the larger isopycnal diffusivity, as isotherms do not follow isopycnals in the upper Indian Ocean north of 10°S. For the 21.0°C isotherm, which closely follows isopycnal 25.0 σ _θ , the heat budget yields a K _θ again of 0.5 cm² s⁻¹, the value of the diapycnal diffusivity.

For the Indian–Pacific system, K _ρ decreases from 1.3 cm² s⁻¹ for 22.0 σ _θ (the warm pool water, depth ∼60 m) to 0.9 cm² s⁻¹ for 23.0 σ _θ (the tropical water between 20°N and 20°S, depth ∼100 m), and to 0.1 cm² s⁻¹ for 25.0 σ _θ (40°N–40°S, depth ∼170 m). In the eastern tropical Pacific, K _ρ = 1.1 cm² s⁻¹ for 21.5 σ _θ (depth ∼25 m) while K _ρ = 0.6 cm² s⁻¹ for 22.0 σ _θ (depth ∼35 m). In the Atlantic, K _ρ = 0.6 cm² s⁻¹ for 24.0 σ _θ between 20°N and 15°S (depth ∼80 m), and 0.2 cm² s⁻¹ for 25.0 σ _θ between 30°N and 35°S (depth ∼120 m). For the water volume bounded by 25.5 σ _θ farther south and north (50°N–40°S), air–sea buoyancy gain in the Tropics is about the size of the buoyancy loss in the subtropics, and the near-zero net flux may not have significance compared to the errors in the data. For 27.5 σ _θ , which encompasses the large region from about 65°N to the Antarctic (with midocean average depth of 400 m), K _ρ is 0.2 cm² s⁻¹. The results indicate that mixing strength generally decreases poleward and with depth in the upper ocean.

Abstract

The relative roles of buoy and Argo observations in two sea surface temperature (SST) analyses are studied in the global ocean and tropical Pacific Ocean over 2000–16 using monthly Extended Reconstructed SST version 5 (ERSSTv5) and Daily Optimum Interpolation SST version 2 (DOISST). Experiments show an overall higher impact by buoys than Argo floats over the global oceans and an increasing impact by Argo floats. The impact by Argo floats is generally larger in the Southern Hemisphere than in the Northern Hemisphere. The impact on trends and anomalies of globally averaged SST by either one is small when the other is used. The warming trend over 2000–16 remains significant by including either buoys or Argo floats or both. In the tropical Pacific, the impact by buoys was large over 2000–05 when the number of Argo floats was low, and became smaller over 2010–16 when the number and area coverage of Argo floats increased. The magnitude of El Niño and La Niña events decreases when the observations from buoys, Argo floats, or both are excluded. The impact by the Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) is small in normal years and during El Niño events. The impact by TAO/TRITON buoys on La Niña events is small when Argo floats are included in the analysis systems, and large when Argo floats are not included. The reason for the different impact on El Niño and La Niña events is that the drifting buoys are more dispersed from the equatorial Pacific region by stronger trade winds during La Niña events.

Abstract

A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE).

The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.

Abstract

The exchange of internal energy between the warm water pools of the tropical oceans and the overlying atmosphere is thought to play a central role in the evolving climate system of the earth. Spatial displacements of the warm water pools are observed on annual and interannual time scales, the latter most notably in the Pacific in association with ENSO. Whether such variations are also associated with net changes in pool energy content is investigated. Extending the work of Niiler and Stevenson and Walin who considered the time mean energy budgets for volumes bounded by an isotherm, the time-dependent version of their equation is analyzed in which the main terms involve the time variations of pool volume and average temperature, net energy exchange between the pool and overlying atmosphere, and the turbulent ocean fluxes across the pool boundaries. The dominant signal in the mean seasonal energy budgets of the warm pools is an approximate balance between the annual variation of air pool heat exchange and the time-varying energy storage; the inferred turbulent ocean heat flux per unit area across the bounding surface of the warm pools is relatively steady through the year. Interannual variations of the warm pools are characterized by changes in pool volumes and temperature on ENSO and longer time scales with indications of an out-of-phase relationship between pool pseudo-energy content and the Southern Oscillation index. The ability to diagnose the varying turbulent ocean fluxes exiting the warm water pools on these time scales was impeded by incompatibilities between ocean temperature data and several air–sea flux climatologies. For the unscaled Coupled Ocean–Atmosphere Data Set (COADS) flux product that yields sensibly downgradient ocean heat flux estimates, strong positive correlation between air pool heat flux and inferred turbulent ocean flux at the pool base on an interannual time scale is found. But, given the uncertainties in the air–sea fluxes, it is difficult to firmly attribute these bottom flux changes to variations in ocean mixing processes. Though disappointing in the short term, it is suggested that time-dependent warm pool energy budget analyses constitute powerful diagnostics for validating future air–sea flux climatologies.

Abstract

NOAA global surface temperature (NOAAGlobalTemp) is NOAA’s operational global surface temperature product, which has been widely used in Earth’s climate assessment and monitoring. To improve the spatial interpolation of monthly land surface air temperatures (LSATs) in NOAAGlobalTemp from 1850 to 2020, a three-layer artificial neural network (ANN) system was designed. The ANN system was trained by repeatedly randomly selecting 90% of the LSATs from ERA5 (1950–2019) and validating with the remaining 10%. Validations show clear improvements of ANN over the original empirical orthogonal teleconnection (EOT) method: the global spatial correlation coefficient (SCC) increases from 65% to 80%, and the global root-mean-square difference (RMSD) decreases from 0.99° to 0.57°C during 1850–2020. The improvements of SCCs and RMSDs are larger in the Southern Hemisphere than in the Northern Hemisphere and are larger before the 1950s and where observations are sparse. The ANN system was finally fed in observed LSATs, and its output over the global land surface was compared with those from the EOT method. Comparisons demonstrate similar improvements by ANN over the EOT method: The global SCC increased from 78% to 89%, the global RMSD decreased from 0.93° to 0.68°C, and the LSAT variability quantified by the monthly standard deviation (STD) increases from 1.16° to 1.41°C during 1850–2020. While the SCC, RMSD, and STD at the monthly time scale have been improved, long-term trends remain largely unchanged because the low-frequency component of LSAT in ANN is identical to that in the EOT approach.

Abstract

Sea surface temperature (SST) observations from satellite-based Advanced Very High Resolution Radiometer (AVHRR) instrument exhibit biases. Adjustments necessary for removing the AVHRR biases have been studied by progressive experiments. These experiments show that the biases are sensitive to various parameters, including the length of the input data window, the base-function empirical orthogonal teleconnections (EOTs), the ship–buoy SST adjustment, and a shift in grid system. The difference in bias adjustments due to these parameters can be as large as 0.3°–0.5°C in the tropical Pacific at the monthly time scale.

The AVHRR bias adjustments were designed differently in the daily optimum interpolation SST (DOISST) and the Extended Reconstructed SST datasets that ingest AVHRR SSTs (ERSSTsat). The different AVHRR bias adjustments result in the differences in SST datasets in DOISST and ERSSTsat. Comparisons show that the SST difference between these two datasets results largely from the difference in the AVHRR bias adjustments and little from SST analysis methods in the Niño-3.4 region, as well as in the global oceans. For example, the average difference of the Niño-3.4 SSTs between DOISST and ERSSTsat is approximately 0.12°C due to the bias adjustments and is about 0.01°C due to the analysis methods.

This study finds that the DOISST datasets can be improved by using the revised AVHRR bias adjustment of a wider input data window, updated EOTs, and a shifted grid system in DOISST. Improvements can also be made by including a ship–buoy SST adjustment, a zonal SST adjustment, or revised EOTs without damping in the high latitudes in ERSSTsat.

Abstract

Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis.

Abstract

The NOAA Daily Optimum Interpolation Sea Surface Temperature dataset (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25° × 0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC. Our assessments indicate that biases and root-mean-square difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; also, RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.

Abstract

Previous research has shown that the 1877/78 El Niño resulted in great famine events around the world. However, the strength and statistical significance of this El Niño event have not been fully addressed, largely due to the lack of data. We take a closer look at the data using an ensemble analysis of the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The ERSSTv5 standard run indicates a strong El Niño event with a peak monthly value of the Niño-3 index of 3.5°C during 1877/78, stronger than those during 1982/83, 1997/98, and 2015/16. However, an analysis of the ERSSTv5 ensemble runs indicates that the strength and significance (uncertainty estimates) depend on the construction of the ensembles. A 1000-member ensemble analysis shows that the ensemble mean Niño-3 index has a much weaker peak of 1.8°C, and its uncertainty is much larger during 1877/78 (2.8°C) than during 1982/83 (0.3°C), 1997/98 (0.2°C), and 2015/16 (0.1°C). Further, the large uncertainty during 1877/78 is associated with selections of a short (1 month) period of raw-data filter and a large (20%) acceptance criterion of empirical orthogonal teleconnection modes in the ERSSTv5 reconstruction. By adjusting these two parameters, the uncertainty during 1877/78 decreases to 0.5°C, while the peak monthly value of the Niño-3 index in the ensemble mean increases to 2.8°C, suggesting a strong and statistically significant 1877/78 El Niño event. The adjustment of those two parameters is validated by masking the modern observations of 1981–2017 to 1861–97. Based on the estimated uncertainties, the differences among the strength of these four major El Niño events are not statistically significant.

Abstract

To facilitate evaluation and monitoring of numerical weather prediction model forecasts and satellite-based products against high-quality in situ observations, a data repository for collocated model forecasts, a satellite product, and in situ observations has been created under the support of various World Climate Research Program (WCRP) working groups. Daily measurements from 11 OceanSITES buoys are used as the reference dataset to evaluate five ocean surface wind products (three short-range forecasts, one reanalysis, and one satellite based) over a 1-yr intensive analysis period, using the WCRP community weather prediction model evaluation metrics. All five wind products correlate well with the buoy winds with correlations above 0.76 for all 11 buoy stations except the meridional wind at four stations, where the satellite and model performances are weakest in estimating the meridional wind (or wind direction). The reanalysis has higher cross-correlation coefficients (above 0.83) and smaller root-mean-square (RMS) errors than others. The satellite wind shows larger variability than that observed by buoys; contrarily, the models underestimate the variability. For the zonal and meridional winds, although the magnitude of biases averaged over all the stations are mostly <0.12 m s⁻¹ for each product, the magnitude of biases at individual stations can be >1.2 m s⁻¹, confirming the need for regional/site analysis when characterizing any wind product. On wind direction, systematic negative (positive) biases are found in the central (east central) Pacific Ocean. Wind speed and direction errors could induce erroneous ocean currents and states from ocean models driven by these products. The deficiencies revealed here are useful for product and model improvement.