Frequency and wavenumber spectra of sea surface height (SSH) and surface geostrophic velocity are presented, as they result for the Atlantic Ocean from a 23 year long altimeter data set and from a hierarchy of ocean model simulations with spatial resolutions of 16, 8, and 4 km. SSH frequency spectra follow a spectral decay of roughly f 21 on long periods; toward higher frequencies a spectral decay close to f 22 is found. For geostrophic velocity spectra, a somewhat similar picture emerges, albeit with flatter spectral relations. In terms of geostrophic velocity wavenumber spectra, we find a general relation close to k 23 in the high-resolution model results. Outside low-energy regions all model spectra come close to observed spectra at low frequencies and wavenumbers in terms of shape and amplitude. However, the highest model resolution appears essential for reproducing the observed spectra at high frequencies and wavenumbers. This holds especially for velocity spectra in mid and high latitudes, suggesting that eddy resolving ocean models need to be run at a resolution of 1/248 or better if one were to fully resolve the observed mesoscale eddy field. Causes for remaining discrepancies between observed and simulated results can be manifold. At least partially, they can be rationalized by taking into account an aliasing effect of unresolved temporal variability in the altimetric observations occurring on periods smaller than the 20 days Nyquist period of the altimetric data, thereby leading to an overestimate of variability in the altimetric estimates, roughly on periods below 100 days. Citation:Biri, S., N. Serra, M. G. Scharffenberg, and D. Stammer (2016), Atlantic sea surface height and velocity spectra inferred from satellite altimetry and a hierarchy of numerical simulations, J.
As decadal predictions become operational, the need to use, understand, and extract information from them becomes essential. A climate index is a simple diagnostic quantity that can be used to characterize integral aspects of a geophysical system such as circulation patterns, and thus can be used to evaluate decadal forecasts. One of the most studied and well documented regions of the World Ocean is the North Atlantic. The North Atlantic subpolar gyre is an important region for the modulation of European climate and where skillful predictions of up to a decade can be obtained. Ocean re‐analysis (ORA‐S4) data from 1959 to 2017 are used to introduce a new methodology to compute a climate index of the North Atlantic subpolar gyre that captures both the variability in its strength and shape, the latter was to our knowledge never investigated previously as part of the variability of the gyre on interannual to decadal time scales. The methodology reveals two states of the gyre (before and after 2000), the former is mainly driven by temperature and the latter by a combination of mechanisms that interact to sustain a relatively stable subpolar gyre in terms of strength.
The turbulent exchanges, or fluxes, of heat, moisture and momentum between the atmosphere and the ocean play a crucial role in the Earth’s climate system. Direct measurements of turbulent fluxes are very challenging and sparse, and do not span the full range of environmental conditions that exist over the ocean. This means that empirical “bulk formulae” parameterizations that relate direct flux observations to concurrent measurements of the mean meteorological and sea surface variables contain considerable uncertainty. In this paper, we present a Python 3.6 (or higher) open-source software package “AirSeaFluxCode” for the computation of the heat (latent and sensible) and momentum fluxes. Ten different parameterizations are included, each based on published descriptions or code and each derived from a different set of observations, or different assumptions about the turbulent exchange processes. They represent a range of current expert opinion on how the fluxes depend on mean properties and can be used to explore uncertainty in calculated fluxes. AirSeaFluxCode also allows the adjustment of the mean meteorological input parameters (air temperature, humidity and wind speed) from the height at which they are obtained to a user-defined output height. This height adjustment enables the comparison of measurements, or model-derived values, made at different heights above sea-level. The parameterizations calculate the fluxes using input parameters that are relatively easily to measure, or are available as model output: wind speed, air temperature, sea surface temperature, atmospheric pressure and humidity. Where original code is available we have compared its output with that of AirSeaFluxCode. Any changes made to increase consistency across algorithms by standardizing computational methods or calculation of meteorological variables, for example, are discussed and the impacts quantified: these are shown to be insignificant except for a few cases where conditions were extreme, and AirSeaFluxCode is shown to be robust. We also investigate the impact on the fluxes caused by different assumptions about the exchange processes, or the choices inherent in the implementation of the parameterizations. For example, sea surface temperature usually refers to data typically obtained at depths of between 1 and 10 m. However, since some parameterizations require a “skin” sea surface temperature, code that adjusts temperature at depth to skin temperature is included: this has a very significant impact on the fluxes. Selecting a parameterization that is appropriate for the available sea surface temperature will avoid the need to adjust the sea temperature data and the uncertainties associated with that adjustment, and will also avoid the biases due to use of the “wrong” measure of temperature. Significant differences also resulted from assumptions about the size of reduction in sea surface humidity to account for salinity effects: the uncertainty in the reduction factor needs to be quantified in future analyses. Fluxes in extreme conditions are particularly uncertain since the transfer coefficients in the different parameterizations vary most at very high and very low wind speeds. Low wind speeds are also challenging for numerical implementation since choices have to be made regarding: convergence criteria for the iterative calculation, inclusion of a parameterization for convective gustiness, or application of ad hoc limits to various parameters. All of these choices can significantly affect the flux estimates for light winds.
Abstract. Low-level easterly winds encircling Antarctica help drive coastal currents which modify transport of circumpolar deep water to ice shelves, and the formation and distribution of sea ice. Reanalysis datasets are especially important at high southern latitudes where observations are few. Here, we investigate the representation of the mean state and short-term variability of coastal easterlies in three recent reanalyses, ERA5, MERRA-2 and JRA-55. Reanalysed winds are compared with summertime marine near-surface wind observations from the Advanced Scatterometer (ASCAT) and surface and upper air measurements from coastal stations. Reanalysis coastal easterlies correlate highly with ASCAT (r= 0.91, 0.89 and 0.85 for ERA5, MERRA-2 and JRA-55, respectively) but notable wind speed biases are found close to the coastal margins, especially near complex orography and at high wind speeds. To characterise short-term variability, 12-hourly reanalysis and coastal station winds are composited using self-organising maps (SOMs), which cluster timesteps under similar synoptic and mesoscale influences. Reanalysis performance is sensitive to the flow configuration at stations near steep coastal slopes, where they fail to capture the magnitude of near-surface wind speed variability when synoptic forcing is weak and conditions favour katabatic forcing. ERA5 exhibits the best overall performance, has more realistic orography, and a more realistic jet structure and temperature profile. These results demonstrate the regime behaviour of Antarctica's coastal winds and indicate important features of the coastal winds which are not well characterised by reanalysis datasets.
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