Sedimentary model and high-frequency cyclicity in a Mediterranean, shallow-shelf, temperate-carbonate environment (uppermost Miocene, Agua Amarga Basin, Southern Spain)

Uppermost Tortonian to lower Messinian temperate carbonates crop out in the Agua Amarga Basin (SE Spain). They consist of four units. The lower three units can be tentatively assigned to the lowstand systems tract of a fourth-order sequence, constituting in turn the lowstand (‘megatrough unit’), transgressive (‘breccia unit’) and highstand (‘bedded unit’) stages of a higher-order cycle. All these materials were deposited in a small pull-apart basin related to the sinistral Carboneras strike-slip fault system. The best represented is the bedded unit (up to 25 m thick), which consists of bioclastic, bryozoan/bivalve-dominated calcarenites/calcirudites with abundant fragments of echinoids, barnacles, benthic foraminifers, coralline algae, brachiopods and solitary corals. Facies trends within this unit are roughly arranged in an E-W direction, with the coastline to the north of the basin. The depositional model is that of a gentle ramp with prograding beaches and shoals in its higher parts. Seaward of the shoals was the ‘factory area’, where most organisms lived and maximum carbonate production took place. From this area some of the skeletons were washed landwards by waves and/or currents during storms and incorporated into the shoals and beaches, and others moved downslope along the ramp as mass-flows, accumulating to form the ‘fan-bedded zone’. The factory-area and fan-bedded sediments intercalate five well-defined, thick beds of calcarenites/fine-grained calcirudites. They show bar morphologies (single or amalgamated), or make up sand-waves with very consistent tabular cross-bedding pointing landwards. These beds formed in a very shallow, wave/current-influenced, coastal environment. The bars and sand waves in the fan-bedded zone developed during lowstands, while those located higher up in the ramp interbedded with the factory facies are related to transgressive stages. Prograding beaches, shoals, factory facies and fan-bedded layers developed during the highstands. Net skeletal production occurred mainly during the highstands. Sediment-accretion values of these sediments are similar to those of present and ancient shallow-marine, temperate carbonates considering that the whole bedded unit was deposited in a 100 000-year interval (equivalent to the short eccentricity cycle). The five cycles inside the bedded unit would therefore correspond to the c. 20 000-year precession cycles of the Milankovitch band.     

弱震活动与续发性地震的可能关系

大震后总有绵延多时的余震,大震前常有弱震活动增强现象.密集分布的弱震往往伴随有续发性强震.本文从弱震活动的能量关系上给出发生续发性地震的可能判据.并阐述了决定序列类型的余震活动所赖以存在的可能地质条件.     

Intermediate time error growth and predictability: tropics versus mid-latitudes

The evolution of identical twin errors from an atmospheric general circulation model is studied in the linear range (small errors) through intermediate times and the approach to saturation. Between forecast day 1 and 7, the normalized error variance in the tropics is similar to that at higher latitudes. After that, tropical errors grow more slowly. The predictability time τ taken for tropical errors to reach half their saturation values is larger than that for mid-latitudes, especially for the planetary waves, thus implying greater potential predictability in the tropics.
The discrepancy between mid-latitude and tropical τ is more pronounced at 850 hPa than at 200 hPa, is largest for the planetary waves, and is more pronounced for errors arising from wave phase differences (than from wave amplitude differences).
The spectra of the error in 200 hPa zonal wind show that for forecast times up to about 5 d, the tropical error peaks at much shorter scales than the mid-latitude errors, but that subsequently tropical and mid-latitude error spectra look increasingly similar.
The difference between upper and lower level tropical τ may be due to the greater influence of mid-latitudes at the upper levels.     

On the selection of prognostic moments in parametrization schemes for drop sedimentation

Common parametrization models for cloud microphysical processes use condensate mass density and/or particle number density as prognostic properties. However, other moments of the particle size distribution can likewise be chosen for prediction. This study deals with parametrization models with one and two, respectively, prognostic moments for the sedimentation of drop ensembles. The spectral resolving model defines the reference solution.
The evolution of the vertical profiles of liquid water content, drop number density and rain rate strongly depend on the choice of the prognostic moments in the parametrization models. In models with a single prognostic moment, its vertical profile is copied by all other moments. The moment of most physical pertinence is recommended for prediction. In models with two prognostic moments, the vertical profiles of all moments differ. The orders of the prognostic moments should be chosen close to the order of moments of highest relevance. Otherwise large errors occur. For example, comparison of modelled versus observed radar reflectivity (6th moment with respect to diameter) does not tell much about the quality of other properties if reflectivity is diagnosed from for example, number density and mass density. Furthermore, mass conservation is fulfilled only if mass density is forecasted.     

Numerical simulations of optical properties of Saharan dust aerosols with emphasis on lidar applications

In the framework of the Saharan Mineral Dust Experiment (SAMUM) for the first time the spectral dependence of particle linear depolarization ratios was measured by combining four lidar systems. In this paper these measurements are compared with results from scattering theory based on the T-matrix method. For this purpose, in situ measurements—size distribution, shape distribution and refractive index—were used as input parameters; particle shape was approximated by spheroids. A sensitivity study showed that lidar-related parameters—lidar ratio   S _p   and linear depolarization ratio  δ _p   —are very sensitive to changes of all parameters. The simulated values of the  δ _p   are in the range of 20% and 31% and thus in the range of the measurements. The spectral dependence is weak, so that it could not be resolved by the measurements. Calculated lidar ratios based on the measured microphysics and considering equivalent radii up to 7.5 μm show a range of possible values between 29 and 50 sr at  λ= 532 nm  . Larger   S _p   might be possible if the real part of the refractive index is small and the imaginary part is large. A strict validation was however not possible as too many microphysical parameters influence   S _p   and  δ _p   that could not be measured with the required accuracy.     

Fine structure of a Greenland reverse tip jet: a numerical simulation

Reverse tip jets are strong low-level winds with easterly component that form near the southern tip of Greenland. In the present study, a reverse tip jet case which occurred from 21 to 22 December 2000 was examined to clarify its fine structure using a numerical model with a horizontal resolution of 3 km. The reverse tip jet, showing the supergeostrophic wind speed with a maximum wind speed in excess of 45 m s⁻¹, extended from the east coast of Greenland to the west of Cape Farewell with anticyclonic curvature. A cloud free region coincided with the jet indicated that there was a mesoscale downdraft. Along the eastern edge of the jet, a banded cloud formed between the upstream easterly wind and the colder northerly wind that is a part of the jet and is located along the east coast. This cloud was associated with large gradients in surface wind speed, temperature, moisture, and heat flux. A maximum surface total heat flux of 300 W m⁻² coincided with the location of the jet. It is suggested that the orographic deflection by Greenland's large-scale topography as well as small-scale downslope winds behind mountains with fiords causes the reverse tip jet.     

Reconciliation of excess 14C-constrained global CO2 piston velocity estimates

Oceanic excess radiocarbon data is widely used as a constraint for air–sea gas exchange. However, recent estimates of the global mean piston velocity  〈 k 〉  from Naegler et al., Krakauer et al., Sweeney et al. and Müller et al. differ substantially despite the fact that they all are based on excess radiocarbon data from the GLODAP data base. Here I show that these estimates of  〈 k 〉  can be reconciled if first, the changing oceanic radiocarbon inventory due to net uptake of CO₂ is taken into account; second, if realistic reconstructions of sea surface  Δ¹⁴C  are used and third, if  〈 k 〉  is consistently reported with or without normalization to a Schmidt number of 660. These corrections applied, unnormalized estimates of  〈 k 〉  from these studies range between 15.1 and 18.2 cm h⁻¹. However, none of these estimates can be regarded as the only correct value for  〈 k 〉  . I thus propose to use the 'average' of the corrected values of  〈 k 〉  presented here (16.5 ± 3.2 cm h⁻¹) as the best available estimate of the global mean unnormalized piston velocity  〈 k 〉  , resulting in a gross ocean-to-atmosphere CO₂ flux of 76 ± 15 PgC yr⁻¹ for the mid-1990s.     

Data assimilation in a system with two scales—combining two initialization techniques

An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observations.     

The maximum likelihood ensemble filter performances in chaotic systems

The performance of the maximum likelihood ensemble filter (MLEF), is investigated in the context of generic systems featuring the essential ingredients of unstable dynamics and on a spatially extended system displaying chaos. The main objective is to clarify the response of the filter to different regimes of motion and highlighting features which may help its optimization in more realistic applications. It is found that, in view of the minimization procedure involved in the filter analysis update, the algorithm provides accurate estimates even in the presence of prominent non-linearities. Most importantly, the filter ensemble size can be designed in connection to the properties of the system attractor (Kaplan–Yorke dimension), thus facilitating the filter setup and limiting the computational cost by using an optimal ensemble. As a corollary, this latter finding indicates that the ensemble perturbations in the MLEF reflect the intrinsic system error dynamics rather than a sampling of realizations of an unknown error covariance.