Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

The Phase A study of the ESA M4 mission candidate ARIEL

ARIEL, the Atmospheric Remote sensing Infrared Exoplanet Large survey, is one of the three M-class mission candidates competing for the M4 launch slot within the Cosmic Vision science programme of the European Space Agency (ESA). As such, ARIEL has been the subject of a Phase A study that involved European industry, research institutes and universities from ESA member states. This study is now completed and the M4 down-selection is expected to be concluded in November 2017. ARIEL is a concept for a dedicated mission to measure the chemical composition and structure of hundreds of exoplanet atmospheres using the technique of transit spectroscopy. ARIEL targets extend from gas giants (Jupiter or Neptune-like) to super-Earths in the very hot to warm zones of F to M-type host stars, opening up the way to large-scale, comparative planetology that would place our own Solar System in the context of other planetary systems in the Milky Way. A technical and programmatic review of the ARIEL mission was performed between February and May 2017, with the objective of assessing the readiness of the mission to progress to the Phase B1 study. No critical issues were identified and the mission was deemed technically feasible within the M4 programmatic boundary conditions. In this paper we give an overview of the final mission concept for ARIEL as of the end of the Phase A study, from scientific, technical and operational perspectives.     

Ancient harbors and Holocene morphogenesis of the Ras Ibn Hani peninsula (Syria)

Ras Ibn Hani peninsula, a wave-dominated tombolo (800 × 1000 m) on the Syrian coast, provides evidence for significant Holocene changes that can be linked to geological inheritance, rising post-glacial sea level, sediment supply and human impacts. Initial development of Ras Ibn Hani's coastal system began ~ 8000 years ago when shallow marine environments formed in a context of rising post-glacial sea level. Following relative sea-level stabilization ~ 6000 cal yr BP, beach facies trace the gradual formation of a wave-dominated sandbank fronted by a ~ 2300 × ~ 500 m palaeo-island whose environmental potentiality was attractive to Bronze Age societies. A particularly rapid phase of tombolo accretion is observed after ~ 3500 cal yr BP characterised by a two- to fourfold increase in sedimentation rates. This is consistent with (i) a pulse in sediment supply probably driven by Bronze Age/Iron Age soil erosion in local catchments, and (ii) positive feedback mechanisms linked to regionally attested neotectonics. Archaeological remains and radiocarbon datings confirm that the subaerial tombolo was probably in place by the Late Bronze Age. These data fit tightly with other eastern Mediterranean tombolo systems suggesting that there is a great deal of predictability to their geology and stratigraphy at the regional scale.     

The ARIEL mission reference sample

The ARIEL (Atmospheric Remote-sensing Exoplanet Large-survey) mission concept is one of the three M4 mission candidates selected by the European Space Agency (ESA) for a Phase A study, competing for a launch in 2026. ARIEL has been designed to study the physical and chemical properties of a large and diverse sample of exoplanets and, through those, understand how planets form and evolve in our galaxy. Here we describe the assumptions made to estimate an optimal sample of exoplanets – including already known exoplanets and expected ones yet to be discovered – observable by ARIEL and define a realistic mission scenario. To achieve the mission objectives, the sample should include gaseous and rocky planets with a range of temperatures around stars of different spectral type and metallicity. The current ARIEL design enables the observation of ～1000 planets, covering a broad range of planetary and stellar parameters, during its four year mission lifetime. This nominal list of planets is expected to evolve over the years depending on the new exoplanet discoveries.     

On the (anticipated) diversity of terrestrial planet atmospheres

On our way toward the characterization of smaller and more temperate planets, missions dedicated to the spectroscopic observation of exoplanets will teach us about the wide diversity of classes of planetary atmospheres, many of them probably having no equivalent in the Solar System. But what kind of atmospheres can we expect? To start answering this question, many theoretical studies have tried to understand and model the various processes controlling the formation and evolution of planetary atmospheres, with some success in the Solar System. Here, we shortly review these processes and we try to give an idea of the various type of atmospheres that these processes can create. As will be made clear, current atmosphere evolution models have many shortcomings yet, and need heavy calibrations. With that in mind, we will thus discuss how observations with a mission similar to EChO would help us unravel the link between a planet’s environment and its atmosphere.     

Stochastic multi-site generation of daily weather data

Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations. This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol 8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed, in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated, while maintaining efficient daily spatial autocorrelations and monthly interstation correlations.     

The contribution of the ARIEL space mission to the study of planetary formation

The study of extrasolar planets and of the Solar System provides complementary pieces of the mosaic represented by the process of planetary formation. Exoplanets are essential to fully grasp the huge diversity of outcomes that planetary formation and the subsequent evolution of the planetary systems can produce. The orbital and basic physical data we currently possess for the bulk of the exoplanetary population, however, do not provide enough information to break the intrinsic degeneracy of their histories, as different evolutionary tracks can result in the same final configurations. The lessons learned from the Solar System indicate us that the solution to this problem lies in the information contained in the composition of planets. The goal of the Atmospheric Remote-Sensing Infrared Exoplanet Large-survey (ARIEL), one of the three candidates as ESA M4 space mission, is to observe a large and diversified population of transiting planets around a range of host star types to collect information on their atmospheric composition. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres, which should show minimal condensation and sequestration of high-Z materials and thus reveal their bulk composition across all main cosmochemical elements. In this work we will review the most outstanding open questions concerning the way planets form and the mechanisms that contribute to create habitable environments that the compositional information gathered by ARIEL will allow to tackle.