scholarly journals The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems

2018 ◽  
Author(s):  
Kamolphat Atsawawaranunt ◽  
Laia Comas-Bru ◽  
Sahar Amirnezhad Mozhdehi ◽  
Michael Deininger ◽  
Sandy P. Harrison ◽  
...  
Keyword(s):  
Working Group ◽  
Climate Models ◽  
Full Range ◽  
Global Changes ◽  
Sufficient Information ◽  
Data Set ◽  
Oxygen And Carbon Isotope ◽  
Cave System ◽  
Out Of Sample ◽  
Age Model

Abstract. Stable isotope records from speleothems provide information on past climate changes, most particularly information that can be used to reconstruct past changes in precipitation and atmospheric circulation. These records are increasingly being used to provide “out-of-sample” evaluations of isotope-enabled climate models. SISAL (Speleothem Isotope Synthesis and Analysis) is an international working group of the Past Global Changes (PAGES) project. The working group aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation. The SISAL database contains data for individual speleothems, grouped by cave system. Stable isotopes of oxygen and carbon (δ18O, δ13C) measurements are referenced by distance from the top or youngest part of the speleothem. Additional tables provide information on dating, including information on the dates used to construct the original age model and sufficient information to assess the quality of each data set and to erect a standardized chronology across different speleothems. The metadata table provides location information, information about the full range of measurements carried out on each speleothem and information about the cave system that is relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at http://dx.doi.org/10.17864/1947.139.

scholarly journals The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems

2018 ◽  
Vol 10 (3) ◽  
pp. 1687-1713 ◽  
Author(s):  
Kamolphat Atsawawaranunt ◽  
Laia Comas-Bru ◽  
Sahar Amirnezhad Mozhdehi ◽  
Michael Deininger ◽  
Sandy P. Harrison ◽  
...  
Keyword(s):  
Working Group ◽  
Climate Models ◽  
Full Range ◽  
Global Changes ◽  
Sufficient Information ◽  
Data Set ◽  
Oxygen And Carbon Isotope ◽  
Cave System ◽  
Out Of Sample ◽  
Age Model

Abstract. Stable isotope records from speleothems provide information on past climate changes, most particularly information that can be used to reconstruct past changes in precipitation and atmospheric circulation. These records are increasingly being used to provide “out-of-sample” evaluations of isotope-enabled climate models. SISAL (Speleothem Isotope Synthesis and Analysis) is an international working group of the Past Global Changes (PAGES) project. The working group aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation. The SISAL database contains data for individual speleothems, grouped by cave system. Stable isotopes of oxygen and carbon (δ18O, δ13C) measurements are referenced by distance from the top or bottom of the speleothem. Additional tables provide information on dating, including information on the dates used to construct the original age model and sufficient information to assess the quality of each data set and to erect a standardized chronology across different speleothems. The metadata table provides location information, information on the full range of measurements carried out on each speleothem and information on the cave system that is relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at https://doi.org/10.17864/1947.147.

scholarly journals Retrieval of water vapor using ground-based observations from a prototype ATOMMS active centimeter- and millimeter-wavelength occultation instrument

2019 ◽  
Vol 12 (3) ◽  
pp. 1955-1977
Author(s):  
Dale M. Ward ◽  
E. Robert Kursinski ◽  
Angel C. Otarola ◽  
Michael Stovern ◽  
Josh McGhee ◽  
...  
Keyword(s):  
Water Vapor ◽  
Climate Models ◽  
Full Range ◽  
Lower Troposphere ◽  
Sufficient Information ◽  
Water Vapor Absorption ◽  
Fundamental Goal ◽  
Vapor Absorption ◽  
Weather And Climate ◽  
Global Coverage

Abstract. A fundamental goal of satellite weather and climate observations is profiling the atmosphere with in situ-like precision and resolution with absolute accuracy and unbiased, all-weather, global coverage. While GPS radio occultation (RO) has perhaps come closest in terms of profiling the gas state from orbit, it does not provide sufficient information to simultaneously profile water vapor and temperature. We have been developing the Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS) RO system that probes the 22 and 183 GHz water vapor absorption lines to simultaneously profile temperature and water vapor from the lower troposphere to the mesopause. Using an ATOMMS instrument prototype between two mountaintops, we have demonstrated its ability to penetrate through water vapor, clouds and rain up to optical depths of 17 (7 orders of magnitude reduction in signal power) and still isolate the vapor absorption line spectrum to retrieve water vapor with a random uncertainty of less than 1 %. This demonstration represents a key step toward an orbiting ATOMMS system for weather, climate and constraining processes. ATOMMS water vapor retrievals from orbit will not be biased by climatological or first-guess constraints and will be capable of capturing nearly the full range of variability through the atmosphere and around the globe, in both clear and cloudy conditions, and will therefore greatly improve our understanding and analysis of water vapor. This information can be used to improve weather and climate models through constraints on and refinement of processes affecting and affected by water vapor.

scholarly journals Machine Learning in Futures Markets

2021 ◽  
Vol 14 (3) ◽  
pp. 119
Author(s):  
Fabian Waldow ◽  
Matthias Schnaubelt ◽  
Christopher Krauss ◽  
Thomas Günter Fischer
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Learning Models ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Arbitrage ◽  
Out Of Sample ◽  
Sample Testing ◽  
Arbitrage Strategy ◽  
Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

scholarly journals Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1477 ◽  
Author(s):  
Davide De Luca ◽  
Luciano Galasso
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Southern Italy ◽  
Regional Climate ◽  
Data Series ◽  
Rainfall Time Series ◽  
Annual Maximum ◽  
Data Set ◽  
Maximum Rainfall ◽  
Rainfall Events

This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

scholarly journals The Development of a Comprehensive Data Set for Systematic Studies of Machine Translation

2021 ◽  
pp. 248-262
Author(s):  
Jörg Tiedemann
Keyword(s):  
Machine Translation ◽  
Training Data ◽  
Sufficient Information ◽  
Data Set ◽  
Neural Machine Translation ◽  
High Resource ◽  
Language Groups ◽  
Comprehensive Data ◽  
Parallel Data ◽  
Initial Results

This paper presents our on-going efforts to develop a comprehensive data set and benchmark for machine translation beyond high-resource languages. The current release includes 500GB of compressed parallel data for almost 3,000 language pairs covering over 500 languages and language variants. We present the structure of the data set and demonstrate its use for systematic studies based on baseline experiments with multilingual neural machine translation between Finno-Ugric languages and other language groups. Our initial results show the capabilities of training effective multilingual translation models with skewed training data but also stress the shortcomings with low-resource settings and the difficulties to obtain sufficient information through straightforward transfer from related languages.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals A database of Holocene nearshore marine mollusc shell geochemistry from the Northeast Pacific

2021 ◽  
Author(s):  
Hannah M. Palmer ◽  
Veronica Padilla Vriesman ◽  
Roxanne M. W. Banker ◽  
Jessica R. Bean
Keyword(s):  
Carbon Isotope ◽  
Heat Waves ◽  
Indigenous Communities ◽  
Geochemical Data ◽  
Shell Middens ◽  
Data Set ◽  
Oxygen And Carbon Isotope ◽  
Northeast Pacific ◽  
Isotope Data ◽  
Δ13c And Δ18o

Abstract. The shells of marine invertebrates can serve as high-resolution records of oceanographic and atmospheric change through time. In particular, oxygen and carbon isotope analyses of nearshore marine calcifiers that grow by accretion over their lifespans provide seasonal records of environmental and oceanographic conditions. Archaeological shell middens generated by Indigenous communities along the Northeast Pacific coast contain shells harvested over multiple seasons for millennia. These shell middens, as well as analyses of archival and modern shells, have the potential to provide multi-site, seasonal archives of nearshore conditions throughout the Holocene. A significant volume of oxygen and carbon isotope data from archaeological shells exists, yet is separately published in archaeological, geochemical, and paleoceanographic journals and has not been comprehensively analyzed to examine oceanographic change over time. Here, we compiled a database of previously published oxygen and carbon isotope data from archaeological, archival, and modern marine molluscs from the North American coast of the Northeast Pacific (32° N to 50° N). This database includes oxygen and carbon isotope data from over 550 modern, archaeological, and sub-fossil shells from 8880 years before present (BP) to the present, from which there are 4,845 total δ13C and 5,071 total δ18O measurements. Shell dating and sampling strategies vary among studies (1–118 samples per shell) and vary significantly by journal discipline. Data are from various bivalves and gastropod species, with Mytilus spp. being the most commonly analyzed taxon. This novel database can be used to investigate changes in nearshore sea surface conditions including warm-cool oscillations, heat waves, and upwelling intensity, and provides nearshore calcite δ13C and δ18O values that can be compared to the vast collections of offshore foraminifera calcite δ13C and δ18O data from marine sediment cores. By utilizing previously published geochemical data from midden and museum shells rather than sampling new specimens, future scientific research can reduce or omit the alteration or destruction of culturally valued specimens and sites. The data set is publicly available through PANGAEA at https://doi.org/10.1594/PANGAEA.932671 (Palmer et al., 2021).

scholarly journals Transient climate simulations of the deglaciation 21–9 thousand years before present; PMIP4 Core experiment design and boundary conditions

2015 ◽  
Vol 8 (10) ◽  
pp. 9045-9102 ◽  
Author(s):  
R. F. Ivanovic ◽  
L. J. Gregoire ◽  
M. Kageyama ◽  
D. M. Roche ◽  
P. J. Valdes ◽  
...  
Keyword(s):  
Working Group ◽  
Ad Hoc ◽  
Climate Models ◽  
Ice Sheet ◽  
Before Present ◽  
Last Deglaciation ◽  
Transient Simulations ◽  
The Last Deglaciation ◽  
Set Up ◽  
Core Simulation

Abstract. The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 thousand years. Here, we present the design of a coordinated Core simulation over the period 21–9 thousand years before present (ka) with time varying orbital forcing, greenhouse gases, ice sheets, and other geographical changes. A choice of two ice sheet reconstructions is given, but no ice sheet or iceberg meltwater should be prescribed in the Core simulation. Additional focussed simulations will also be coordinated on an ad-hoc basis by the working group, for example to investigate the effect of ice sheet and iceberg meltwater, and the uncertainty in other forcings. Some of these focussed simulations will focus on shorter durations around specific events to allow the more computationally expensive models to take part.

scholarly journals Meteorological, soil moisture, surface water, and groundwater data from the St Denis National Wildlife Area, Saskatchewan, Canada

2018 ◽  
Author(s):  
Edward K. P. Bam ◽  
Rosa Brannen ◽  
Sujata Budhathoki ◽  
Andrew M. Ireson ◽  
Chris Spence ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Surface Water ◽  
Climate Models ◽  
Multiple Scales ◽  
Full Range ◽  
Research Network ◽  
Groundwater Levels ◽  
Groundwater Availability ◽  
Surface Water And Groundwater

Abstract. Long-term meteorological, soil moisture, surface water, and groundwater data provide information on past climate change, most notably information that can be used to analyze past changes in precipitation and groundwater availability in a region. These data are also valuable to test, calibrate and validate hydrological and climate models. CCRN (Changing Cold Regions Network) is a collaborative research network that brought together a team of over 40 experts from 8 universities and 4 federal government agencies in Canada for 5 years (2013–18) through the Climate Change and Atmospheric Research (CCAR) Initiative of the Natural Sciences and Engineering Research Council of Canada (NSERC). The working group aimed to integrate existing and new data with improved predictive and observational tools to understand, diagnose and predict interactions amongst the cryospheric, ecological, hydrological, and climatic components of the changing Earth system at multiple scales, with a geographic focus on the rapidly changing cold interior of Western Canada. The St Denis National Wildlife Area database contains data for the prairie research site, St Denis National Wildlife Research Area, and includes atmosphere, soil, and groundwater. The meteorological measurements are observed every 5 seconds, and half-hourly averages (or totals) are logged. Soil moisture data comprise volumetric water content, soil temperature, electrical conductivity and matric potential for probes installed at depths of 5 cm, 20 cm, 50 cm, 100 cm, 200 cm and 300 cm in all soil profiles. Additional data on snow surveys, pond and groundwater levels, and water isotope isotopes collected on an intermittent basis between 1968 and 2018 are also presented including information on the dates and ground elevations (datum) used to construct hydraulic heads. The metadata table provides location information, information about the full range of measurements carried out on each parameter and GPS locations that are relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at https://doi.org/10.20383/101.0115.

scholarly journals Development of a daily gridded precipitation data set for the Middle East

2008 ◽  
Vol 12 ◽  
pp. 165-170 ◽  
Author(s):  
A. Yatagai ◽  
P. Xie ◽  
P. Alpert
Keyword(s):  
Middle East ◽  
Climate Models ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Data Set ◽  
Key Points ◽  
Fertile Crescent ◽  
Precipitation Analysis ◽  
The Impact

Abstract. We show an algorithm to construct a rain-gauge-based analysis of daily precipitation for the Middle East. One of the key points of our algorithm is to construct an accurate distribution of climatology. One possible advantage of this product is to validate high-resolution climate models and/or to diagnose the impact of climate changes on local hydrological resources. Many users are familiar with a monthly precipitation dataset (New et al., 1999) and a satellite-based daily precipitation dataset (Huffman et al., 2001), yet our data set, unlike theirs, clearly shows the effect of orography on daily precipitation and other extreme events, especially over the Fertile Crescent region. Currently the Middle-East precipitation analysis product is consisting of a 25-year data set for 1979–2003 based on more than 1300 stations.

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