Abstract

Differences between Quick Scatterometer (QuikSCAT) level-2b wind vectors from the Jet Propulsion Laboratory [JPL; the Direction Interval Retrieval with Thresholded Nudging (DIRTH) product] and from the Remote Sensing Systems Co. (RSS; smoothed versions 3.0 and 4.0) for one sample month are presented. Each dataset is derived from the same observations, but processing methods result in differences between wind vectors. These differences originate from 1) uncertainty in the geophysical model functions that relate backscatter to wind, 2) noise in the backscatter measurements, and 3) spatial filtering. Statistics of wind vector differences from RSS and JPL are used as an indication of structural uncertainty in QuikSCAT wind retrievals. When grouped by 1 m s⁻¹ bins, systematic differences are largest beyond 20 m s⁻¹, where wind speeds from version 3.0 (version 4.0) of RSS can be more than 15 (10) m s⁻¹ higher (lower) than JPL wind speeds. Below 20 m s⁻¹, systematic differences on the order of tenths of a meter per second are attributed to differences in the retrieval methods, rain and ice contamination, and cross-swath position. Even once the recommended data flags are applied, differences in individual wind speed retrievals exceed 10 m s⁻¹ in a few cases but are much smaller in regions of the swath for which the viewing geometry allows more reliable retrievals. In all parts of the swath, the standard deviations of the differences are smaller than 1.0 m s⁻¹. The analyses provide a measure of the structural uncertainty in QuikSCAT wind velocity that is due to the retrieval process, although such comparisons are not able to determine which dataset is closest to the actual wind.

Abstract

Beaufort equivalent scales from the literature have been compared to find the scale that gives the most homogenous, internally consistent, combined anemometer and visual wind speed data from the Comprehensive Ocean–Atmosphere Data Set. Anemometer wind speeds have been height corrected using the individual anemometer height for each ship, where that could be identified, resulting in a more consistent dataset than that used in previous studies. Monthly mean 1° averages were constructed for visual and anemometer wind speeds separately from data between 1980 and 1990. The anemometer and visual means were compared where there were enough observations to give confidence in both means. The equivalent scale of Lindau was the most effective at giving similar anemometer and visual wind distributions from this mean dataset. The scale of daSilva et al. also performed well. The Lindau scale is, however, preferred because of its more rigorous derivation. The results for the different scales are in agreement with Lindau’s suggestion that the characteristic biases of earlier Beaufort scales could be explained by the statistical method of derivation.

Abstract

To assess climatic changes in sea surface temperature (SST), changes in the measurement method with time and the effect of these changes on the mean SST must be quantified. Observations from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) have been analyzed for the period from 1970 to 1997 using both SST measurement metadata contained within the dataset and a World Meteorological Organization (WMO) catalog of observing ships. The WMO metadata were particularly important in identifying engine-intake SSTs during the 1970s, but increased method identification over the entire period. There are strong regional variations in the preferred SST measurement method, with engine-intake SST most common in the Pacific and bucket SST preferred by countries bordering the Atlantic. The number of engine-intake SSTs increases over time and becomes more numerous than buckets by the early 1980s.

There are significant differences between SST observations made by different methods. The rounding of reports is more common for engine-intake SST than for either bucket or hull sensor SST, which degrades its quality. Significant time-varying biases exist between SST derived from buckets and from engine intakes. The SST difference has a strong seasonal signal with bucket SST being relatively cold in winter, probably resulting from heat loss from the buckets, and warm in summer, probably resulting from solar warming or the sampling of a shallow warm layer. There is also a long-term trend with engine-intake SST being relatively warm in the early period but with a small annual mean difference between the two methods by 1990.

Abstract

Random observational errors for sea surface temperature (SST) are estimated using merchant ship reports from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) for the period of 1970–97. A statistical technique, semivariogram analysis, is used to isolate the variance resulting from the observational error from that resulting from the spatial variability in a dataset of the differences of paired SST reports. The method is largely successful, although there is some evidence that in high-variability regions the separation of random and spatial error is not complete, which may have led to an overestimate of the random observational error in these regions. The error estimates are robust to changes in the details of the regression method used to estimate the spatial variability.

The resulting error estimates are shown to vary with region, time, the quality control applied, the method of measurement, the recruiting country, and the source of the data. SST data measured using buckets typically contain smaller random errors than those measured using an engine-intake thermometer. Errors are larger in the 1970s, probably because of problems with data transmission in the early days of the Global Telecommunications System. The best estimate of the global average random error in ICOADS ship SST for the period of 1970–97 is 1.2°C if the estimates are weighted by ocean area and 1.3°C if the estimates are weighted by the number of observations.

Abstract

A method is developed to quantify systematic errors in two types of sea surface temperature (SST) observations: bucket and engine-intake measurements. A simple linear model is proposed where the SST measured using a bucket is cooled or warmed by a fraction of the air–sea temperature difference and the SST measured using an engine intake has a constant bias. The model is applied to collocated nighttime observations made at moderate wind speeds, allowing the effects of solar radiation and strong vertical gradients in the upper ocean to be neglected. The analysis is complicated by large random errors in all of the variables used. To estimate coefficients in this model, a novel type of linear regression, where errors in two variables are correlated with each other, is introduced. Because of the uncertainty in a priori estimates of the error covariance matrix, a Bayesian analysis of the regression problem is developed, and maximum likelihood approximations to the posterior distributions of the model parameters are obtained.

Results show that the temperature change in bucket SST resulting from the air–sea temperature difference can be detected. The analysis suggests that bucket SST may be in error by a fraction from 0.12° ± 0.02° to 0.16° ± 0.02°C of the air–sea temperature difference. When this temperature change of the bucket SST is accounted for, a warm bias in engine-intake SST in the mid- to late 1970s and the 1980s was found to be smaller than that suggested by previous studies, ranging between 0.09° ± 0.06° and 0.18° ± 0.05°C. For the early 1990s the model suggests that the engine-intake SSTs may have a cold bias of −0.13° ± 0.07°C.

Abstract

The effect of heating due to solar radiation on measurements of humidity obtained from ships is examined. Variations in wet- and dry-bulb temperature measured on each side of a research ship are shown to correlate with solar radiation. However, the resulting variations in the measured humidity are shown to be negligible (less than 0.1 g kg⁻¹). Any radiation induced humidity errors in data from merchant ships participating in the Voluntary Observing Ship Special Observing Programme for the North Atlantic are also shown to be small, less than 0.2 g kg⁻¹. For calculation of the latent heat flux, the near-surface humidity should be calculated from the observed wet- and dry-bulb temperatures, whereas the dry-bulb temperature should be corrected for radiation-induced errors before calculation of the atmospheric stability or the sensible heat flux.

The exchange, or flux, of heat between the oceans and atmosphere is an important driver of the global oceanic and atmospheric circulations but remains poorly quantified. Direct measurement of heat flux remains a research activity and so global heat flux datasets are generated using observations of winds, air and sea temperatures, and humidity as input to heat flux parameterizations known as “bulk formulas.” We remain dependent on the observations from merchant ships in the Voluntary Observing Ships (VOS) program, which are archived in the International Comprehensive Ocean-Atmosphere Dataset (ICOADS); measurements from buoys are sparse and satellites cannot accurately recover all the variables required for heat flux calculation.

Careful analysis of VOS data is necessary to produce gridded datasets of meteorological variables and fluxes with the accuracy required for climate research. Past in situ flux datasets have averaged observations on monthly timescales to reduce random uncertainty. It has therefore been hard to understand the contributions to observed variability from measurement errors, poor sampling, or natural variability. The new dataset, which covers the period 1973 to 2006, avoids this problem by first constructing daily mean fields using optimal interpolation. This allows each component of variability to be handled correctly and, for the first time, uncertainty estimates to be produced. New bias adjustments have also been developed and applied. The new dataset is described and a preliminary comparison with flux estimates from moored buoys, satellites, and atmospheric reanalysis models is presented.

Abstract

Previously published estimates of the surface turbulent fluxes over the North Atlantic Ocean have been compared by applying the calculation methods used by each author to a common dataset of ship observations. The major differences between the various flux estimations were due to whether or not the data were corrected for observation height, the method of calculating averaged fluxes and the choice of transfer coefficient.

By correcting the ship observations obtained from the Voluntary observing ship Special Observing Programme–North Atlantic for known observational biases an estimate of the correct values for the fluxes has been made. The previous studies were found to have overestimated the North Atlantic sea to air heat transfer by up to 30%. All of the schemes overestimated the annual cycle, giving fluxes approximately equal to the best estimate values in the summer but much higher fluxes in the winter. The use of transfer coefficient values much larger than the values determined by air-sea interaction experiments could not be justified by either the effects of measurement errors or by fair weather bias. The lower turbulent flux values are compatible with a balanced North Atlantic heat budget, given the uncertainty in the other flux terms.

Abstract

Results from an analysis of the Southampton Oceanography Centre (SOC) global air–sea heat flux climatology, which has been calculated using in situ weather reports from voluntary observing ships covering the period 1980–93, are presented. Systematic errors in the fluxes arising from differences in observing procedure have been quantified and corrected; the magnitude of these errors is up to 15 W m⁻² with strong seasonal and regional variations. Despite these corrections, closure of the ocean heat budget is not obtained as the global mean net heat flux is an oceanic gain of 30 W m⁻². The validity of closing the heat budget by global scaling of the flux components is assessed by comparison of the SOC flux fields with Woods Hole Oceanographic Institute research buoy measurements. The level of agreement between the two is found to vary from one site to another. Thus, closure of the ocean heat budget requires regional adjustments to the flux components in order to avoid significant biases in the adjusted fields. Close agreement is found for several buoys deployed in the Subduction Array off the coast of northwest Africa. However, at other buoy deployment sites in the western equatorial Pacific warm pool and south of Bermuda in the North Atlantic, the flux adjustment improves the estimate of the net heat exchange. Further evidence for regional biases is obtained from a comparison of box mean surface heat fluxes derived from hydrographic section data with the corresponding SOC values in the Atlantic and North Pacific. The climatological heat loss is found to be an underestimate in those boxes containing the strongest surface flux expression of the major western boundary currents.

Abstract

The effects on a dataset of smoothing by successive correction have been investigated. The resulting spatial resolution is estimated using a distribution of ship reports from a sample month. Although the smoothing uses the same characteristic radii over the whole globe, the resulting resolution is spatially variable and, in data-sparse regions, will show large month-to-month variability with changes in the distribution of the ship tracks. The climatological dataset, which is gridded at 1°, is shown to have a typical resolution of 3°. In some regions the resolution is much coarser.

Using sea surface temperature as an example, it is shown that the successive correction procedure as used, for example, in a recent climatological dataset, is not successful in removing all of the noise in data-sparse regions. Additionally, the well-defined intermonthly variability in the main shipping lanes, where there are many observations, is degraded by the influence of poorer-quality data in the surrounding regions. This typically increases the intermonthly variability estimates in the shipping lanes by a factor of 2. Further, the reduction of intermonthly variability, by up to a factor of 6, in highly variable regions such as the Gulf Stream, is greater than can be accounted for by noise in the individual ship reports. This reduction is due to the removal of small-scale variability by the smoothing process. Removal of coherent and persistent small-scale variability has an effect on the temporal and spatial characteristics of the data. It is suggested that smoothing by successive correction, although commonly used, is poorly suited to such spatially inhomogenous data as those from the merchant ships.

However, the effect of successive correction on variability analysis using empirical orthogonal functions (EOFs) is shown to be small for the most significant modes of variability identified in the Gulf Stream region. This is because the EOF analysis picks out the large-scale variability in the highest-order modes. However, too large a fraction of the total variance explained is ascribed to the large-scale modes of variability. Variability with small spatial scales is more likely to be significant if raw data are used in the EOF analysis. Little significance should be given to EOF modes with spatial scales similar to the size of gaps between shipping lanes; this varies from region to region.