New insights into geochemical behaviour in ancient marine carbonates (Upper Jurassic Ammonitico Rosso): Novel proxies for interpreting sea‐level dynamics and palaeoceanography

Elemental concentrations in Phanerozoic sea water are known to fluctuate both in time and space. With regard to carbonates precipitated from marine fluids, elemental concentrations in the carbonate crystal lattice are affected by a complex array of equilibrium and non‐equilibrium as well as post‐depositional alteration processes. To assess the potential of carbonate elemental chemostratigraphy, seven Upper Jurassic sections were investigated along a proximal to distal transect across the south‐east Iberian palaeomargin. The aim was to explore stratigraphic and spatial variations in calcium, strontium, magnesium, iron and manganese elemental abundances. The epicontinental geochemical record is influenced by the combination of continental runoff and a significant diagenetic overprint. In contrast, the epioceanic geochemical record agrees with reconstructed open marine sea water values, reflecting a moderate degree of syn‐depositional to early marine pore water diagenesis. Establishing a fair degree of preservation of matrix micrite, a thorough statistical approach was applied and elemental associations tested for their environmental significance. Principal component and hierarchical cluster analyses revealed a persistent relation between carbonate magnesium, iron and strontium abundances. Processes related to early diagenetic nodulation in Ammonitico Rosso facies most probably account for the incorporation of these elements in the calcium carbonate lattice. The clear decoupling of carbonate manganese abundance with respect to the remaining elements is documented and related to high sea floor spreading rates and hydrothermal activity during the Late Jurassic. The investigation of potential time‐fluctuation of geochemical patterns was approached through variogram computation. The observed temporal behaviour is most likely to be forced by relative sea‐level dynamics, reflecting Late Jurassic palaeoceanographic conditions and potential planetary interactions. The data obtained in this study highlight the utility of elemental data from carbonate matrix micrites as geochemical proxies for studying the influence of remote trigger factors.     

Recent Near-surface Temperature Trends in the Antarctic Peninsula from Observed,Reanalysis and Regional Climate Model Data

This study investigates the recent near-surface temperature trends over the Antarctic Peninsula.We make use of available surface observations,ECMWF’s ERA5 and its predecessor ERA-Interim,as well as numerical simulations,allowing us to contrast different data sources.We use hindcast simulations performed with Polar-WRF over the Antarctic Peninsula on a nested domain configuration at 45 km(PWRF-45)and 15 km(PWRF-15)spatial resolutions for the period 1991?2015.In addition,we include hindcast simulations of KNMI-RACMO21P obtained from the CORDEX-Antarctica domain(~50 km)for further comparisons.Results show that there is a marked windward warming trend except during summer.This windward warming trend is particularly notable in the autumn season and likely to be associated with the recent deepening of the Amundsen/Bellingshausen Sea low and warm advection towards the Antarctic Peninsula.On the other hand,an overall summer cooling is characterized by the strengthening of the Weddell Sea low as well as an anticyclonic trend over the Amundsen Sea accompanied by northward winds.The persistent cooling trend observed at the Larsen Ice Shelf station is not captured by ERA-Interim,whereas hindcast simulations indicate that there is a clear pattern of windward warming and leeward cooling.Furthermore,larger temporal correlations and lower differences exhibited by PWRF-15 illustrate the existence of the added value in the higher spatial resolution simulation.     

Potential for small scale added value of RCM’s downscaled climate change signal

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ～5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ～5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.     

Nonparametric analysis of high wind speed data

In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.     

Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models

This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons.