Sea Surface Temperature Sensitivity to Water Turbidity from Simulations of the Turbid Black Sea Using HYCOM*

This study describes atmospheric forcing parameters constructed from different global climatologies, applied to the Black Sea, and investigates the sensitivity of Hybrid Coordinate Ocean Model (HYCOM) simulations to these products. Significant discussion is devoted to construction of these parameters before using them in the eddy-resolving (≈3.2-km resolution) HYCOM simulations. The main goal is to answer how the model dynamics can be substantially affected by different atmospheric forcing products in the Black Sea. Eight wind forcing products are used: four obtained from observation-based climatologies, including one based on measurements from the SeaWinds scatterometer on the Quick Scatterometer (QuikSCAT) satellite, and the rest formed from operational model products. Thermal forcing parameters, including solar radiation, are formed from two operational models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Fleet Numerical Meteorology and Oceanography Center (FNMOC) Navy Operational Global Atmospheric Prediction System (NOGAPS). Climatologically forced Black Sea HYCOM simulations (without ocean data assimilation) are then performed to assess the accuracy and sensitivity of the model sea surface temperature (SST) and sea surface circulation to these wind and thermal forcing products. Results demonstrate that the model-simulated SST structure is quite sensitive to the wind and thermal forcing products, especially near coastal regions. Despite this sensitivity, several robust features are found in the model SST in comparison to a monthly 9.3-km-resolution satellite-based Pathfinder SST climatology. Annual mean HYCOM SST usually agreed to within ≈±0.2° of the climatology in the interior of the Black Sea for any of the wind and thermal forcing products used. The fine-resolution (0.25° × 0.25°) wind forcing from the scatterometer data along with thermal forcing from NOGAPS gave the best SST simulation with a basin-averaged rms difference value of 1.21°C, especially improving model results near coastal regions. Specifically, atmospherically forced model simulations with no assimilation of any ocean data suggest that the basin-averaged rms SST differences with respect to the Pathfinder SST climatology can vary from 1.21° to 2.15°C depending on the wind and thermal forcing product. The latter rms SST difference value is obtained when using wind forcing from the National Centers for Environmental Prediction (NCEP), a product that has a too-coarse grid resolution of 1.875° × 1.875° for a small ocean basin such as the Black Sea. This paper also highlights the importance of using high-frequency (hybrid) wind forcing as opposed to monthly mean wind forcing in the model simulations. Finally, there are large variations in the annual mean surface circulation simulated using the different wind sets, with general agreement between those forced by the model-based products (vector correlation is usually >0.7). Three of the observation-based climatologies generally yield unrealistic circulation features and currents that are too weak.

Ocean models need over-ocean atmospheric forcing. However, such forcing is not necessarily provided near the land–sea boundary because 1) the atmospheric model grid used for forcing is frequently much coarser than the ocean model grid, and 2) some of the atmospheric model grid over the ocean includes land values near coastal regions. This paper presents a creeping sea-fill methodology to reduce the improper representation of scalar atmospheric forcing variables near coastal regions, a problem that compromises the usefulness of the fields for ocean model simulations and other offshore applications. For demonstration, atmospheric forcing variables from archived coarse-resolution gridded products—the 1.125° × 1.125° 15-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-15) and 1.0° × 1.0° Navy Operational Global Atmospheric Prediction System (NOGAPS)—are used here. A fine-resolution [1/25° × 1/25° cos(lat)], (longitude × latitude) (∼3.2 km) eddy-resolving Black Sea Hybrid Coordinate Ocean Model (HYCOM) is then forced with/without sea-filled atmospheric variables from these gridded products to simulate monthly mean climatological sea surface temperature (SST). Using only over-ocean values from atmospheric forcing fields in the ocean model simulations significantly reduces the climatological mean SST bias (by ∼1°–3°C) and rms SST difference over the seasonal cycle (by ∼2°–3°C) in coastal regions. Performance of the creeping sea-fill methodology is also directly evaluated using measurements of wind speed at 10 m above the surface from the SeaWinds scatterometer on the NASA Quick Scatterometer (QuikSCAT) satellite. Comparisons of original monthly mean wind speeds from operational ECMWF and NOGAPS products with those from QuikSCAT give basin-averaged rms differences of 1.6 and 1.4 m s−1, respectively, during 2000–03. Similar comparisons performed with sea-filled monthly mean wind speeds result in a much lower rms difference (0.7 m s−1 for both products) during the same time period, clearly confirming the accuracy of the methodology even on interannual time scales. Most of the unrealistically low wind speeds from ECMWF and NOGAPS near coastal boundaries are appropriately corrected with the use of the creeping sea fill. Wind speed errors for ECWMF and NOGAPS (mean bias of ≥ 2.5 m s−1 with respect to QuikSCAT during 2000–03) are substantially eliminated (e.g., almost no bias) near most of the land–sea boundaries. Finally, ocean, atmosphere, and coupled atmospheric–oceanic modelers need to be aware that the creeping sea fill is a promising methodology in significantly reducing the land contamination resulting from an improper land–sea mask existing in gridded coarse-resolution atmospheric products (e.g., ECMWF).

Daily inter-annual simulations of SST and MLD using atmospherically forced OGCMs: Model evaluation in comparison to buoy time series

Optimizing surface winds using QuikSCAT measurements in the Mediterranean Sea during 2000–2006

[1] Matsoukas et al. [2007] present a monthly analysis of heat fluxes in relation to heat budget in the Red and Black Seas to provide further insight for air-sea exchange processes in the small ocean basins. Components of net surface heat flux are illustrated during 1984–1995. In computing latent and sensible heat fluxes, Matsoukas et al. [2007] apply traditional bulk formulations. A heat balance method that is based on the available energy for evaporation flux is also presented to compare latent heat fluxes with those from the bulk formulations. All near-surface atmospheric variables, including wind speed at 10 m, used in the heat balance method are obtained from reanalysis of a numerical weather product (NWP). Initial input data for radiation flux calculations are at resolutions of 1.0° and 2.5°, depending on the availability. Monthly means of heat budget components are computed on the basis of monthly means of atmospheric variables during 1984–2000. [2] Concerning heat fluxes in the Red and Black Seas, all computations and their details are generally properly presented by Matsoukas et al. [2007]. We worry, however, about one major concern (section 3 and 4). We also identify two other potentially important issues and suggest how these might alter results (section 5). Here our major focus is that Matsoukas et al. [2007] do not consider the representativeness errors inherent in using input data from a coarse resolution NWP product in studying air-sea exchange processes in small ocean basins, such as the Red and Black Seas. This neglection poses serious errors in heat budget computations. For example, the Red Sea is a narrow inland water body which has relatively shallow water depths (Figure 1). It is roughly 1900 km long and, at its widest point, over 300 km wide. Oceanic features are greatly influenced by seasonally reversing winds in this small region so interactions between the ocean shelf and the adjacent continent are of great interest. Therefore, wind speed at 10 m is expected have great influence on latent and sensible heat fluxes especially near coastlines. Use of improper winds for heat flux computations can thus misleadingly influence heat budget estimates as will be described in section 3. In addition, the 2.5° resolution study used in their data cannot accurately represent sea-only radiative fluxes for the small Red and Black Seas. [3] The data for surface atmospheric variables in calculating sensible and latent heat fluxes in the paper by Matsoukas et al. [2007] are obtained from a NWP product, namely, European Centre for Medium-Range Weather Forecasts. Unfortunately, the gridded global fields provided by the NWP centers are generally at a spatial scale too coarse to appropriately define the contrast between water and land grid points for small ocean basins, such as the Red and Black Seas. Such NWP products are given in Table 1. [4] Our focus will be on quantifying the accuracy of the wind speed at 10 m from coarse resolution NWP products, including European Centre for Medium-Range Weather Forecasts 40-year Reanalysis (ERA-40) as used by Matsoukas et al. [2007]. We particularly use wind speed at 10 m for such evaluations since accuracy of latent and sensible heat flux computations strongly depends on accuracy of winds in the bulk parameterizations. For validations of NWP winds a relatively finer gridded wind product, Quick Scatterometer (QSCAT), is formed from satellite measurements (Table 1). In particular, twice-daily QSCAT wind measurements are obtained from Remote Sensor Systems (RSS), http://www.remss.com, and monthly means of rain-free winds are formed. Data for NWP products are obtained from the National Center for Atmospheric Research (NCAR) data support section (http://dss.ucar.edu/datasets/), and monthly means are constructed using 6 hourly values. [5] Several problems arise when using wind speed from the 1.125° × 1.125° resolution (≈125 km) ERA-40 product in computing latent and sensible heat fluxes in the Red Sea especially near the coastal boundaries. At best there can be no more than two grid points in the horizontal direction (Figure 1). Matsoukas et al. [2007] use a 1° grid resolution, and for a specific latitude, the longitude span was between 1 and 3 grid points for the Red Sea. In any case, winds at most grid points near the land-sea boundaries over the sea are contaminated by land values due to relatively coarse resolution, i.e., a grid point includes wind values from both land and ocean, affecting the accuracy of the computed latent and sensible heat fluxes. [6] The land-sea mask values from all NWP products, including the 1.125° resolution ERA-40 on the Gaussian grid, are interpolated to a finer grid of 1/12° (Figure 2a). This is done in order to demonstrate land contamination of winds near the coastal regions and also their accuracies in the interior. The ocean and land areas in NWP products are typically defined by a land-sea mask of zeros and ones, indicating that a grid cell is treated as all sea or all land, respectively. A percentage land-sea mask value of 40 (100), for example, in Figure 2 explains that wind speed at 10 m over the sea are 40% (100%) contaminated by winds over land. [7] The land-sea mask for QSCAT has a different meaning since all satellite measurements are over the sea, and thus there is no land contamination. We describe the satellite-based QSCAT mask as 1.0 for data void areas and 0.0 for regions with valid QSCAT winds. On a month by month basis the mask could vary depending on the orbital pattern. A land-sea mask value of 1.0 for QSCAT winds indicates that there are no valid measurements in that region, in that particular month; this most commonly occurs near the coast. Note that the scatterometer cannot make reliable measurements within 35 km of the coast. [8] In Figure 2a, the most obvious feature of the land-sea mask values is that the land contamination from NWP products exists near all coastal boundaries of the Red Sea. This is especially seen from National Centers for Environmental Prediction (NCEP), which has the coarsest grid resolution. The land-sea mask from ERA-40 also reveals lots of land contamination near the coastal boundaries with only a small white region in the interior where there is no land contamination. This indicates that sensible and latent heat flux computations presented in the paper by Matsoukas et al. [2007] are all based on land-contaminated wind speed values. It is emphasized that in grid cells near the coastline ERA-40 provides both land values and sea values with the final cell-averaged value based on the fraction of land. ERA-40 only has true sea-only values where its land-sea mask is exactly zero, which is far away from the coast. For all products, also including QSCAT, there is no confidence in the accuracy of wind in the northernmost end of the Red Sea on the basis of the land-sea masks because of their insufficient grid resolution. [9] Spatial variations of wind speed at 10 m from QSCAT are compared to those from NWP products during February and August of 2001 (Figures 2b, 2c, 2d, and 2e). These two months are chosen for illustrative purposes. Figure 2b is identical to Figure 2c except that the former also includes winds over land. Winds from QSCAT are available starting from July 1999 onward, but winds from the ERA-40 reanalysis are not available beyond September 2002. For comparison purposes, we therefore choose the year 2001. Satellite-based QSCAT winds are taken as truth, justified because there is no land contamination in the QSCAT winds and they are more reliable than NWP winds near the land-sea boundaries over the global ocean [Kara et al., 2008a]. [10] For NWP winds in February, the consequences from land contamination are severe especially for Navy Operational Global Atmospheric Prediction System (NOGAPS) and ERA-40 (Figure 2b). Since wind speeds over land are low (e.g., <3 m s-1), land contamination makes wind speed weaker over the sea near the coastal boundaries. Wind speed estimates over coastal waters are relatively weaker than those in the interior. NCEP winds over land near the coastal boundary are weak as well, but they are stronger than those from NOGAPS and ERA-40. Thus, relatively coarse resolution NCEP winds have low contrast between land and sea points. This low contrast diminishes the impact from the land contamination in NCEP. Similar features are also evident in August (Figure 2d). [11] Winds from all products are generally uniform over the Red Sea during February (Figure 2c) and August (Figure 2e) of 2001. The common feature of NWP winds is that they are all weaker than the satellite-based QSCAT winds not only near the coastal boundaries but also in the interior, especially in the northern part of the region where QSCAT winds are much stronger (>2 m s−1). Weaker winds from NWP products also exist in other months (not shown). [12] Differences between NWP and QSCAT winds are computed to examine typical biases for the fields shown in Figures 2b and 2c. Winds from NWP products are almost always weaker than those from QSCAT over the Red Sea with some exceptions (Figure 3). Differences in the interior are ≈1 m s−1 and can even be larger in the case of ERA-40 during both February and August of 2001. Near the coastal boundaries of the Red Sea, differences from QSCAT are >3–4 m s−1, particularly for ERA-40 winds, because of the relatively large land contamination of winds over the sea. [13] As demonstrated above, winds from QSCAT are stronger than those from ERA-40. Here, we have a demonstration of what percentage of this is due to the resolution/land mask issue (and other regional effects) and how much is due to the fact that QSCAT winds are normally stronger the ERA-40 winds. Using a 1° × 1° uniform grid over the Red Sea, basin average of the QSCAT wind speeds is 6.4 m s−1 in February of 2001. Because all QSCAT wind measurements are only over water, they are not affected by land contamination. Basin average of original wind speeds from ERA-40 is 3.9 m s−1, and this includes the effects of land contamination. The original ERA-40 winds have a 39% (1-(3.9 m s−1/6.4 m s−1)) low bias relative to QSCAT. [14] If one tries to exclude land contamination by using the sea-fill methodology [Kara et al., 2007a], basin average of the ERA-40 wind speeds becomes 5.3 m s−1, a 17% low bias relative to QSCAT. Note that the creeping sea-fill technique makes use of only over-sea values of any given scalar atmospheric variable (e.g., wind speed here) and replaces the value associated with each land-masked point by one using only nearby sea values [see also Kara et al., 2008a]. In other words, wind speeds shown in the white regions of the land-sea mask of ERA-40 (see Figure 2a) are interpolated to the coastal boundaries. The program for the creeping sea-fill methodology is available online at http://www7320.nrlssc.navy.mil/nasec/. Thus, 56% (1-(17%/39%)) of the original ERA-40 low wind speed bias is due to land contamination that is remedied by the application of the creeping sea-fill in February of 2001. The remainder of the low bias is not attributable to the land contamination as it is evident in the interior of the Red Sea, i.e., even away from land, ERA-40 winds are 17% weaker than QSCAT. [15] One of the most important variables in determining the heat budgets in the Red and Black Seas is latent heat flux. As discussed in the preceding section (Figure 3), in comparison to fine resolution QSCAT, there are large errors in winds from NWP products near the coastal boundaries of the Red Sea. Because latent heat flux is a direct function of wind speed at 10 m in the bulk formulation, we examine whether the resulting heat budget can be significantly affected by ignoring the land contamination, as was done by Matsoukas et al. [2007]. On the basis of new calculations, updated values for the heat budget components are also provided for the Red and Black Seas. [16] To demonstrate the impact of land contamination on latent heat flux, we compute climatological monthly means on the basis of 6 hourly outputs from ERA-40 reanalysis from 1 September 1978 to 1 September 2002 (25 years). We started from 1978 rather than 1957, when the original ERA-40 analysis started, because most of the satellite data used in the assimilation procedure became available around 1979. Climatological means are computed at two grid resolutions: 1° × 1° grids to be consistent with Matsoukas et al. [2007] and 1/12° × 1/12° grids to further include coastal regions of the Red Sea. [17] As an example, climatological mean of latent heat flux is examined in February. In addition to original values directly obtained from ERA-40 at 1° and 1/12° resolutions (Figures 4a and 4c), we also show sea-filled latent heat fluxes for the corresponding fields (Figures 4b and 4d) after the creeping sea-fill methodology described earlier is applied. The land contamination is so severe near the coastal boundaries of the Red Sea that values of latent heat fluxes that had been exceedingly low (e.g., 50 W m−2) become very high (e.g., 200 W m−2) after the creeping sea-fill, especially in the northernmost areas. Excessively low latent heat fluxes from the original ERA-40 reanalysis can easily be attributed to unrealistically weak winds from ERA-40 (Figure 3a). The creeping sea-fill reduces land contamination from latent heat fluxes at both grid resolutions (Figures 4b and 4d). [18] The importance of using a fine resolution ocean grid of 1/12° as opposed to a coarse grid of 1° in obtaining basin-averaged latent heat fluxes is evident in Table 2. Although original ERA-40 values can be quite different, sea-filled values agree with each other quite well. Annually, differences between original and and sea-filled latent heat flux values can be as large as 50 W m−2. Matsoukas et al. [2007] do not have any specific consideration for the land contamination. As expected, such contamination from the 1° gridded field is relatively large. While it is not shown here, land contamination of radiation fluxes is less severe (<30 W m−2). [19] One may notice that the sea-filled latent heat climatologies look different from original fields calculated from ERA-40 (Figure 4). This can be explained as follows. On the basis of the land-sea mask of ERA-40 (Figure 2a) there is essentially one single ERA-40 sea value for each of its latitude bands in the Red Sea, and a few bands have two values). This means that the across the sea variation in ERA-40 is entirely from land/sea differences. Using the sea-fill approach, we assume the land is like the closest sea point. Therefore, there is no across the sea variation in this case. This is clearly seen in the plot south of 22°N. North of 22°N, one sees some across the sea variation (where ERA-40 has two grid points over sea). However, the sea grid points are also much more consistent (blue) and significantly different from the nearby land points. The blue values are in the all-values plots, but swamped by other color contours from land. The impact of land contamination due to coarse grid resolutions of NWP products is also discussed in the Black Sea [Kara et al., 2008b]. [20] Finally, for completeness we present net heat budgets for the Red and Black Seas (Table 3). For each component of the net heat budget, sea-filled values are first produced on the basis of the land-sea mask of ERA-40 (Figure 2a). Climatological means are then computed on the basis of 6 hourly values during 1978–2002. It is quite remarkable that when using the sea-filled ERA-40 data, the heat budget is successfully closed in the Black Sea with a net value of 0 W m−2. The heat budget in the Red Sea is also almost closed with a net value of −8 W m−2. [21] In this commentary, so far our major focus has been on the land contamination issues, resulting in improper estimates of the heat budget in the Red and Black Seas. There are two more additional points. First important isssue is that all results presented in the paper by Matsoukas et al. [2007] are derived using monthly means of atmospheric variables to compute monthly mean latent and sensible heat fluxes. In traditional climate studies [e.g., Josey et al., 1999; Boyer et al., 2006], high-frequency observations of wind speed and other atmospheric variables are used to make estimates of heat flux which are then averaged to compute monthly means. Matsoukas et al. [2007] could have used the 6 hourly NWP products to estimate 6 hourly latent and sensible heats fluxes from which monthly means could be calculated. Using monthly mean atmospheric variables in computing “monthly” fluxes can result in ≈20% difference depending on the time and location [e.g., Lee et al., 2005; Gulev, 1997]. [22] Another fundamental problem in the paper by Matsoukas et al. [2007] is that in computing latent and sensible heat fluxes they use outdated parameterizations for exchange coefficients [Vardavas, 1987]. Their study assumes neutral atmospheric conditions for each month, and the above mentioned monthly winds are preferred in these calculations. However, there has been significant progress in determining exchange coefficients since 1987. For example, the well-known and commonly used Couped Ocean-Atmosphere Response Experiment (COARE) algorithm (v3.0) provides up-to-date exchange coefficient parameterizations for computing latent and sensible heat fluxes [Fairall et al., 2003]. Results based on the COARE algorithm clearly reveal that ignoring the effects of vapor mixing ratio in the parameterization of exchange coefficient can give a flux value that is ≈3–5 times less than its actual value at very low wind speeds [Kara et al., 2005]. This is further illustrated at the web page http://www7320.nrlssc.navy.mil/nasec/. Using only wind speed and ignoring air-sea stratification (i.e., neutral case) can also cause errors as large as 20% or more in the wind stress exchange coefficient even on monthly means [Kara et al., 2007b]. [23] Results presented by Matsoukas et al. [2007] ignore land contamination in obtaining the heat budget for the Red and Black Seas. We demonstrate that near coastal boundaries, the spatially coarse NWP products have errors not only in the wind fields. Because of large errors (>3 m s−1) in ERA-40 winds, corrections to winds are essential in the heat budget computations. Postprocessing of winds and other NWP fields can reduce errors in latent and sensible heat fluxes near the land-sea boundaries. We found that ignoring the land contamination on latent heat fluxes could result in basin-averaged errors as high as 50 W m−2, significantly changing the heat budget. The errors near the coastline can often be >100 W m−2. As it is demonstrated in this paper, sea-filled shortwave and longwave radiation values processed from ERA-40 and interpolated to fine resolution ocean grids (1/12°) are sufficiently accurate since the net surface heat budget is almost closed in both regions. [24] One of the main goals of their study is to demonstrate that the energy balance method disagrees with the bulk aerodynamic approach. However, the wind errors in their application of the bulk formulation make it difficult to justify comparison between the two methodologies. Specifically, some values of heat budget components presented by Matsoukas et al. [2007] are somewhat consistent with ours shown in Table 3 but this appears to result from a cancellation of errors. For example, by using improper exchange coefficients they may have obtained relatively higher latent and sensible heat fluxes. The use of land-contaminated winds will lead to low estimates of latent heat flux. Errors in other terms would have to compensate by inflating the latent heat flux. In addition, monthly mean winds used for computing monthly mean heat fluxes would lead to additional errors in the resulting heat budget. Our estimates are also based on slightly longer time period of 1978–2001. [25] Satellite-based QSCAT winds can be used for estimating heat budgets in the future. While the available time period of 1999 onward for QSCAT winds may not be long enough for climatological studies, such a fine resolution gridded (0.25°) product would provide spatial resolution more appropriate for the narrow Red and Black Seas. In addition, land contamination in atmospheric variables needs to be taken into consideration whenever NWP products are used for computing heat budgets over sea-only locations. [26] A. Wallcraft of NRL is greatly appreciated for his helpful comments. The authors acknowledge the invaluable suggestions provided by the reviewer. Additional thanks go to C. Matsoukas for numerous discussions. This work is funded by the Office of Naval Research (ONR) under the 6.2 project, Improved Synthetic Ocean Profiles (ISOP). The paper is contribution NRL/JA/7320/08/8084 and has been approved for public release. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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Evaluation of the effect of meteorological data resolution on Lagrangian particle dispersion simulations using the ETEX experiment

A hybrid Lagrangian–Eulerian model for calculating the trajectories of near-surface drifters in the ocean is developed in this study. The model employs climatological, near-surface currents computed from a spline fit of all available drifter velocities observed in the Pacific Ocean between 1988 and 1996. It also incorporates contemporaneous wind fields calculated by either the U.S. Navy [the Navy Operational Global Atmospheric Prediction System (NOGAPS)] or the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to 30 drifters launched in the tropical Pacific Ocean in three clusters during 1990, 1993, and 1994. For 10-day-long trajectories the forecasts computed by the hybrid model are up to 164% closer to the observed trajectories compared to the trajectories obtained by advecting the drifters with the climatological currents only. The best-fitting trajectories are computed with ECMWF fields that have a temporal resolution of 6 h. The average improvement over all 30 drifters of the hybrid model trajectories relative to advection by the climatological currents is 21%, but in the open-ocean clusters (1990 and 1993) the improvement is 42% with ECMWF winds (34% with NOGAPS winds). This difference between the open-ocean and coastal clusters is due to the fact that the model does not presently include the effect of horizontal boundaries (coastlines). For zero initial velocities the trajectories generated by the hybrid model are significantly more accurate than advection by the mean currents on time scales of 5–15 days. For 3-day-long trajectories significant improvement is achieved if the drifter's initial velocity is known, in which case the model-generated trajectories are about 2 times closer to observations than persistence. The model's success in providing more accurate trajectories indicates that drifters' motion can deviate significantly from the climatological current and that the instantaneous winds are more relevant to their trajectories than the mean surface currents. It also demonstrates the importance of an accurate initial velocity, especially for short trajectories on the order of 1–3 days. A possible interpretation of these results is that winds affect drifter motion more than the water velocity since drifters do not obey continuity.

This study is a multimetric statistical evaluation of interannual and climatological mean sea surface temperature (SST) over the Pacific Ocean (north of 20°S) simulated by an ocean model. The evaluation procedure is outlined using daily and monthly SSTs from eddy‐resolving (0.08°) Hybrid Coordinate Ocean Model (HYCOM). Satellite‐based products and buoy measurements are used for model‐data comparisons. Three are three principal findings. (1) Using monthly mean climatological atmospheric forcing with the addition of a 6‐hourly wind component can yield realistic simulations of monthly mean climatological SST in comparison with observations and interannually forced simulations. (2) Nondimensional skill score can be a very useful metric for validating SST from an ocean model in a large region, such as the Pacific Ocean, where the amplitude of the SST seasonal cycle has large spatial variations. The use of skill score is extensively discussed along with its advantages over other traditional metrics. Interannual model‐data comparisons (1993–2003) using satellite‐based SST give basin‐averaged yearly mean skill score values ranging from 0.35 to 0.58 for HYCOM. (3) A comparison of HYCOM to 804 yearlong daily buoy SST time series spanning 1990–2003 gives a median root mean square value of 0.83°C. Relatively small SST biases and high skill values are essential prerequisites for SST assimilation using an ocean model as a first guess and for SST forecasting. The validation procedures presented in this paper include a variety of statistical metrics and use a comprehensive observational buoy data set. Such procedures can be applied to any global‐ or basin‐scale ocean general circulation model that predicts SST.

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.

In 1991, Typhoon Nat over the western North Pacific made four directional reversals due to its interactions with two other tropical cyclones (TCs), Luke and Mireille. This paper analyzes the performance of three global and two regional models in predicting the movement of Nat to determine the extent to which each of the models was capable of correctly simulating such binary interactions. The global models include those of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.K. Meteorological Office (UKMO) and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS). The regional models studied are the Typhoon Model (TYM) of the Japan Meteorological Agency and the One-Way Tropical Cyclone Model (OTCM) of the U.S. Navy. It was found that in general the global models made better predictions than the regional ones, especially when the large-scale flow was well defined. During the interaction periods, the UKMO model and the TYM were the best. The ECMWF model was also...

Interannual and climatological variations of wind stress drag coefficient (CD) are examined over the global ocean from 1998 to 2004. Here CD is calculated using high temporal resolution (3- and 6-hourly) surface atmospheric variables from two datasets: 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and 2) the Navy Operational Global Atmospheric Prediction System (NOGAPS). The stability-dependent CD algorithm applied to both datasets gives almost identical values over most of the global ocean, confirming the validity of results. Overall, major findings of this paper are as follows: 1) the CD value can change significantly (e.g., &amp;gt;50%) on 12-hourly time scales around the Kuroshio and Gulf Stream current systems; 2) there is strong seasonal variability in CD, but there is not much interannual change in the spatial variability for a given month; 3) a global mean CD ≈ 1.25 × 10−3 is found in all months, while CD ≥ 1.5 × 10−3 is prevalent over the North Pacific and North Atlantic Oceans and in southern high-latitude regions as well, and CD ≤ 1.0 × 10−3 is typical in the eastern equatorial Pacific cold tongue; and 4) including the effects of air–sea stability on CD generally causes an increase of &amp;gt;20% in comparison to the one calculated based on neutral conditions in the tropical regions. Finally, spatially and temporally varying CD fields are therefore needed for a variety of climate and air–sea interaction studies.

Value of bulk heat flux parameterizations for ocean SST prediction

The Navy Operational Global Atmospheric Prediction System (NOGAPS) has proven itself to be competitive with any of the large forecast models run by the large operational forecast centers around the world. The navy depends on NOGAPS for an astonishingly wide range of applications, from ballistic winds in the stratosphere to air-sea fluxes to drive ocean general circulation models. Users of these applications will benefit from a better understanding of how a system such as NOGAPS is developed, what physical assumptions and compromises have been made, and what they can reasonably expect in the future as the system continues to evolve. The discussions will be equally relevant for users of products from other large forecast centers, e.g., National Meteorological Center, European Centre for Medium-Range Weather Forecasts. There is little difference in the scientific basis of the models and the development methodologies used for their development. However, the operational priorities of each center and t...

Fleet Numerical Meteorology and Oceanography Center (FNMOC) Is in the process of transitioning its operational models suite from a Cray C90 shared memory architecture to SGI Origin 2000 (O2K) and Origin 3000 (O3K) distributed memory systems. As part of this transition, the third generation WAVEWATCH III (WW3) wave model is currently being implemented to replace the third generation Wave Model (WAM). WW3 offers several advantages over WAM. For example, WW3 has a third-order accurate wave propagation scheme that reduces the numerical diffusion of swell energy characteristic of the first-order scheme used In WAM. Also, WW3 has been programmed to run efficiently on distributed memory computers. The global Implementation of WW3 was ported to the O2K in December 2000, with atmospheric forcing provided by the FNMOC Navy Operational Global Atmospheric Prediction System (NOGAPS) model. Regional versions of WW3 have also been implemented on the O3K computer in six areas of naval Interest. The wave models were run in parallel two months and the results are compared to wave height measurements. WW3 became operational at FNMOC, replacing WAM, In August of 2001.

The U.S. Navy's new three-dimensional variational analysis system NAVDAS became operational at Fleet Numerical Meteorology and Oceanography Center (FNMOC) on October 1, 2003, paving the way for the direct assimilation of NOAA AMSU-A radiances with the Navy Operational Global Atmospheric Prediction System (NOGAPS). AMSU-A radiance assimilation, which became operational at FNMOC on June 9, 2004, leads to significant improvement in forecast skill, as compared with assimilation of NESDIS ATOVS retrievals. The two- to five-day forecast skill at 500 hPa is increased by 3-10 hours in the Northern Hemisphere, and by 12-20 hrs in the Southern Hemisphere, with similar improvements at 1000 hPa. Forecasts with AMSU-A are consistently better, with fewer forecast "busts", fewer synoptic errors and a general strengthening of the circulations in both hemispheres. Overall, NAVDAS analyses and forecasts with AMSU-A exhibit better fit with radiosondes and other observations. Observations from AMSU-B, which are sensitive to the vertical distribution of water vapor in the troposphere, are used to compute 1DVAR humidity retrievals. NAVDAS assimilation of AMSU-B retrievals into NOGAPS dries out the middle and upper troposphere, and strengthens moisture gradients such as the Intertropical Convergence Zone, correcting known model tendencies. Tropical cyclone track and intensity predictions are slightly improved. Transition of AMSU-B retrieval assimilation to operations at FNMOC is targeted for early 2005.