Ralf Lindau

A downscaling scheme for atmospheric variables to drive soil–vegetation–atmosphere transfer models

For driving soil–vegetation–transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called ‘deterministic’ part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400mhorizontal grid spacing).Anovel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Errors of Atlantic Air–Sea Fluxes Derived from Ship Observations

Using individual ship reports of the Comprehensive Ocean‐Atmosphere Data Set (COADS) monthly 1 83 18 averages of the air‐sea flux fields in the Atlantic are computed to investigate the variance on a seasonal-tointerannual timescale. As an exemplary parameter the latent heat flux is chosen. The total temporal variance at each grid point is split up into four components: the error of the longtime mean, the seeming extramonthly variability, the mean error variance of monthly means, and the intramonthly variance. The spatial distributions of these components are discussed. In most regions the extramonthly variability is dominated by the error. One grid point in the North Atlantic is investigated in more detail. Even in this region of highest data density within the central Atlantic, it turns out that more than half of the temporal variance is caused by the errors of the monthly means.

Zeitabhängige Kalibrierung der Beaufort-Skala für Klimauntersuchungen an historischen Windschätzungen auf See

Klima~inderungen werden heute vielfach untersucht. Meist steht dabei eine mOgliche, durch zus~itzliche Treibhausgase verursachte Erw~rmung im Vordergrund. Diese globale Erw~irmung wird voraussichtlich regional und jahreszeitlich unterschiedlich sein. Die damit bewirkte Ver~inderung der Klimagiirtel der Erde wird mit einer Anderung der Lage und Intensit~it der atmosph~irischen Zirkulation einhergehen. Anderungen der Windsysteme auf den Ozeanen sind ftir Inselund Ktistenbewohner von besonderem Interesse, da sich damit auch die Wahrscheinlichkeiten ftir das Auftreten yon schweren Sttirmen auf See und Sturmfluten sowie von tropischen Wirbelsttirmen ~indern k6nnen, fiber die Windverh~iltnisse auf See liegen seit etwa 1820 Schiffsbeobachtungen vor, die seit 1860 systematisch gesammelt wurden. Leider ist eine direkte Messung des Windes auf fahrenden Schiffen besonders schwierig. Dies gilt gleichermaBen ft~r Schiffe unter Segel wie ftir moderne Schiffe mit stOrenden Aufbauten. Deshalb wird seit mehr als hundert Jahren die Windst~irke auf See nach der Beaufort-Skala gesch~tzt. Da diese Skala zun~ichst nach der Segelf0hrung einer bestimmten Klasse von Schiffen definiert war, kam es mit dem Wandel der Schiffstypen und mit dem Aufkommen der Dampfund Motorschiffe zu einer schleichenden Ver~nderung der Skala. Erst ab 1927 wurde die Skala auf Merkmale des Seegangs bezogen. Dies macht es erforderlich, ftir Klimauntersuchungen Zeitreihen der Windgeschwindigkeit auf versteckte Anderungen zu untersuchen und ggf. die Zuordnung zwischen Beaufort-Sch~itzung und Windgeschwindigkeit zu rekonstruieren. Figur 2 zeigt gesch~itzte Winde aus einem Seegebiet vor der stidamerikanischen Atlantikktiste (Fig. 1). Einer langfristigen Abnahme um mehr als 2 m/s vor dem Zweiten Weltkrieg folgt

Net Air-Sea Heat Flux and Meridional Heat Transport

The net heat gain of the ocean is determined by summing the fluxes of shortwave (SR) and longwave radiation (LR) and the turbulent fluxes of sensible (H) and latent heat (LE) at the sea surface.

Derivation of Air Sea Fluxes by Parameterisations

NHF = SR + LR + LE + H

Benchmarking homogenization algorithms for monthly data

(20)

A New Beaufort Equivalent Scale

Individual ship reports from the Comprehensive Ocean-Atmosphere Data Set (COADS) of the Atlantic Ocean from 1940 to 1979 are used for this climatological study. Maps of monthly mean distribution are calculated not only for the basic ship observations as wind speed or air pressure but also for the indirectly derived climate variables such as evaporation or wind stress, which are obtained by parameterisations. For the derived variables the individual method is used that conserves the effects of cross-correlations. The classical climatological method, that uses averaged values of the meteorological parameters, cannot account for these contributions. For the North East Atlantic JOSEY & al. (1995) showed that the latent heat flux is overestimated by the crude climatological method. The negative correlation between wind speed and sea-air humidity difference causes an overestimation from 1–2 Wm−2 in winter to 7 Wm−2 in summer.

Benchmarking monthly homogenization algorithms

Air-sea interaction is brought about by fluxes of momentum, heat and matter. Since these are not measured routineously, they are determined by parameterisations. In the following, we give a short account of the formulations that we selected.