Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM

A fast coupled global climate model (CGCM) is used to study the sensitivity of El Ni?o Southern Oscillation (ENSO) characteristics to a new interactive flux correction scheme. With no flux correction applied our CGCM reveals typical bias in the background state: for instance, the cold tongue in the tropical east Pacific becomes too cold, thus degrading atmospheric sensitivity to variations of sea surface temperature (SST). Sufficient atmospheric sensitivity is essential to ENSO. Our adjustment scheme aims to sustain atmospheric sensitivity by counteracting the SST drift in the model. With reduced bias in the forcing of the atmosphere, the CGCM displays ENSO-type variability that otherwise is absent. The adjustment approach employs a one-way anomaly coupling from the ocean to the atmosphere: heat fluxes seen by the ocean are based on full SST, while heat fluxes seen by the atmosphere are based on anomalies of SST. The latter requires knowledge of the model??s climatological SST field, which is accumulated interactively in the spin-up phase (??training??). Applying the flux correction already during the training period (by utilizing the evolving SST climatology) is necessary for efficiently reducing the bias. The combination of corrected fluxes seen by the atmosphere and uncorrected fluxes seen by the ocean implies a restoring mechanism that counteracts the bias and allows for long stable integrations in our CGCM. A suite of sensitivity runs with varying training periods is utilized to study the effect of different levels of bias in the background state on important ENSO properties. Increased duration of training amplifies the coupled sensitivity in our model and leads to stronger amplitudes and longer periods of the Nino3.4 index, increased emphasis of warm events that is reflected in enhanced skewness, and more pronounced teleconnections in the Pacific. Furthermore, with longer training durations we observe a mode switch of ENSO in our model that closely resembles the observed mode switch related to the mid-1970s ??climate shift??.     

Quantifying internal variability in a regional climate model: a case study for Southern Africa

This study aims at presenting various methodologies to separate the reproducible and irreproducible components of seasonal and intraseasonal climate variability simulated by a regional climate model over Southern Africa (south of 15°S), during an austral summer rainy season representative of the climatology. To that end, a 30-member ensemble simulation is performed using WRF laterally forced by the ERA40 reanalyses. Retained metrics include the analysis of weather regimes, signal-to-noise ratio, inter-member standard deviation and coefficient of variation. At the seasonal timescale, simulated rainfall amounts generally show a strong reproducibility, except in the subtropics and over the southern part of the Mozambique Channel. There, the number of rainy days is roughly similar in all members, while their average intensity varies extensively. At the intraseasonal timescale, the chronology of weather regimes, derived from the 500?hPa geopotential height, is highly reproducible. Rainfall variability is much less reproducible, especially in the central parts of the domain and near its outflow boundaries. Analysis of a South African regional index nonetheless indicates that both wet and dry spells tend to be accurately simulated and occur in phase in most members, demonstrating that they are embedded in large-scale variability patterns. Internal variability is lastly related to the lateral forcings along the domain boundaries. An objective classification of inflow/outflow mass fluxes allows identification of the recurrent synoptic configurations that favor strong or weak regional reproducibility. The main uncertainties concern the basic morphological features of rain-bearing systems (i.e., their spatial extension, location and propagation speed). Consequences for tropical-temperate interactions are then discussed.     

Rotational atmospheric circulation during North Atlantic-European winter: the influence of ENSO

The dominant variability modes of the North Atlantic-European rotational flow are examined by applying a principal component analysis (PCA/EOF) to the 200?hPa streamfunction mid-winter anomalies (Jan?CFeb monthly means). The results reveal that, when this norm is used, the leading mode (EOF1) does not correspond to the traditional North Atlantic Oscillation (NAO, which appears in our analysis as the second leading mode, EOF2) but is the local manifestation of the leading hemispheric streamfunction EOF. The regression of this regional mode onto the global SST field exhibits a clear El Ni?o signature, with no signal over the Atlantic, while the associated upper height anomalies resemble the Tropical/Northern Hemisphere (TNH) pattern. East of North America, this TNH-like wavetrain produces a meridional dipole-like pattern at lower levels. Although in some ways this pattern resembles the NAO (EOF2), the dynamics of these two modes are very different in that only EOF2 is associated with a latitudinal shift of the North Atlantic stormtrack. Thus, the choice of the streamfunction norm in the EOF analysis allows the separation of two different phenomena that can produce similar dipolar surface pressure anomalies over the North Atlantic but that have different impact on European climate. These two modes also differ on their contribution to variability at lower levels: while NAO-EOF2 is mostly confined to the North Atlantic, TNH-EOF1 has a more annular, global character. At upper levels NAO-EOF2 also produces a global pattern but with no annular structure, reminiscent of the ??circumglobal?? teleconnection.     

High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: how much small-scale information is added by a regional model?

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5° latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP/Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20?km resolution for 16 summer seasons (1990?C2005). The RSM produces realistic details in the regional summer precipitation at 20?km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93% (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92% (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.     

Ecological status of seagrass ecosystems: An uncertainty analysis of the meadow classification based on the Posidonia oceanica multivariate index (POMI)

Quantifying the uncertainty associated with monitoring protocols is essential to prevent the misclassification of ecological status and to improve sampling design. We assessed the Posidonia oceanica multivariate index (POMI) bio-monitoring program for its robustness in classifying the ecological status of coastal waters within the Water Framework Directive. We used a 7-year data set covering 30 sites along 500 km of the Catalonian coastline to examine which version of POMI (14 or 9 metrics) maximises precision in classifying the ecological status of meadows. Five factors (zones within a site, sites within a water body, depth, years and surveyors) that potentially generate classification uncertainty were examined in detail. Of these, depth was a major source of uncertainty, while all the remaining spatial and temporal factors displayed low variability. POMI 9 matched POMI 14 in all factors, and could effectively replace it in future monitoring programs.     

Site effects of the Roio basin, L��Aquila

During the microzonation studies of the April 6th, 2009 L??Aquila earthquake, we observed local seismic amplifications in the Roio area??a plane separated from L??Aquila city center by mount Luco. Six portable, digital instruments were deployed across the plain from 15 April to mid-May 2009. This array recorded 152 aftershocks. We analyzed the ground motion from these events to determine relative site amplification within the plain and on surrounding ridges. Horizontal over vertical spectral ratio on noise data (HVSRN), aftershock recordings (HVEQ) and standard spectral ratio (SSR) showed amplifications at 1.3 and 4.0?Hz on quaternary deposits. Seismic amplifications in the frequency range of 4 and 6?Hz were also observed on a carbonate ridge of Colle di Roio, on the northwestern border of the plateau. A small amplification was noticed near the top of mount Luco, another rocky site. Large discrepancies in the amplification levels between methods have been observed for these sites, but the HVSRN, HVEQ and SSR gave similar results at the stations located in the Roio plain. On the rocky sites, the SSR was more reliable than the HVSRN at estimating the transfer function of the site, even if the resonance frequency seemed to be well detected by the latter method.     

Compositional Bayesian indicator estimation

Indicator kriging is widely used for mapping spatial binary variables and for estimating the global and local spatial distributions of variables in geosciences. For continuous random variables, indicator kriging gives an estimate of the cumulative distribution function, for a given threshold, which is then the estimate of a probability. Like any other kriging procedure, indicator kriging provides an estimation variance that, although not often used in applications, should be taken into account as it assesses the uncertainty of the estimate. An alternative approach to indicator estimation is proposed in this paper. In this alternative approach the complete probability density function of the indicator estimate is evaluated. The procedure is described in a Bayesian framework, using a multivariate Gaussian likelihood and an a priori distribution which are both combined according to Bayes theorem in order to obtain a posterior distribution for the indicator estimate. From this posterior distribution, point estimates, interval estimates and uncertainty measures can be obtained. Among the point estimates, the median of the posterior distribution is the maximum entropy estimate because there is a fifty-fifty chance of the unknown value of the estimate being larger or smaller than the median; that is, there is maximum uncertainty in the choice between two alternatives. Thus in some sense, the latter is an indicator estimator, alternative to the kriging estimator, that includes its own uncertainty. On the other hand, the mode of the posterior distribution estimator, assuming a uniform prior, is coincidental with the simple kriging estimator. Additionally, because the indicator estimate can be considered as a two-part composition which domain of definition is the simplex, the method is extended to compositional Bayesian indicator estimation. Bayesian indicator estimation and compositional Bayesian indicator estimation are illustrated with an environmental case study in which the probability of the content of a geochemical element in soil being over a particular threshold is of interest. The computer codes and its user guides are public domain and freely available.     

Adapting a texture synthesis algorithm for conditional multiple point geostatistical simulation

Computer vision provides several tools for analyzing and simulating textures. The principles of these techniques are similar to those in multiple-point geostatistics, namely, the reproduction of patterns and consistency in the results from a perceptual point of view, thus, ensuring the reproduction of long range connectivity. The only difference between these techniques and geostatistical simulation accounting for multiple-point statistics is that conditioning is not an issue in computer vision. We present a solution to the problem of conditioning simulated fields while simultaneously honoring multiple-point (pattern) statistics. The proposal is based on a texture synthesis algorithm where a fixed search (causal) pattern is used. Conditioning is achieved by adding a non-causal search neighborhood that modifies the conditional distribution from which the simulated category is drawn, depending on the conditioning information. Results show an excellent reproduction of the features from the training image, while respecting the conditioning information. Some issues related to the data structure and to the computer efficiency are discussed.     

Monitoring estuaries using non-permanent stations: practical aspects and data examples

This paper deals with the performances of non-permanent environmental monitoring stations when recording at high-sampling interval (hour) over a long term (months, years) within estuarine waters. Information about data quality and system maintenance requirements are provided based on the experience gained with an autonomous station deployed during 2?years at the Guadiana Estuary (southern Portugal–Spain border). The station includes (1) a multi-parameter probe for water quality (temperature, conductivity, dissolved oxygen, turbidity, chlorophyll and pH) inserted in a tube through a surface-floating buoy; and, (2) a bottom-mounted current meter. The main issues for the continuous acquisition of valid data were biofouling on the optical (chlorophyll, turbidity, dissolved oxygen), conductivity sensors of the probe and high sediment dynamics at the bed. The definition of a detailed maintenance programme is required for the lengthening of (valid) data time series. The typical variability of the parameters is described at seasonal and tidal time scales and episodic events are identified (coastal upwelling and high freshwater inputs). These examples illustrate how integrated data analysis is fundamental to define and understand the changes induced by specific events on several interrelated parameters, and, more generally, how these systems can contribute to a better understanding of the hydro-ecological processes operating in estuaries.