High-resolution monthly precipitation and temperature time series from 2006 to 2100.
Ontology highlight
ABSTRACT:
SUBMITTER: Karger DN
PROVIDER: S-EPMC7378208 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
High-resolution monthly precipitation and temperature time series from 2006 to 2100.
Karger Dirk Nikolaus DN Schmatz Dirk R DR Dettling Gabriel G Zimmermann Niklaus E NE
Scientific data 20200723 1
Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally ...[more]
Similar Datasets
Project description:In the paper, we present a dataset of long gridded time series of monthly minimum, maximum and mean air temperature and atmospheric precipitation for Ukraine (one of the largest European countries), covering the period of 1946-2020. The dataset was built through the thorough historical climate data processing, which included all mandatory steps: data rescue/digitization of missing values and/or periods in station time series from paper sources, their quality control (QC) and homogenization, and finally interpolation on 0.1o × 0.1o grid. The station data comprised monthly values of 178 stations for air temperature (for each of three parameters) and 224 stations for atmospheric precipitation. The quality assurance and homogenization were performed by means of the widely used homogenization software HOMER (HOMogEnization in R), while the well-known interpolation software MISH (Meteorological Interpolation based on Surface Homogenized data basis) was used to perform the gridding. The dataset has the potential to be used in a wide variety of applications, such as regional climate change and variability studies, assessment of other climate gridded products (e.g., reanalysis or analysis), global/regional climate model output verification, etc.
| S-EPMC9469657 | biostudies-literature
Project description:The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatology Network (GHCN), we compare the accuracy of TMPA 3B43 versus the new HRAC-Precip product. The results show an improvement of the mean absolute error of 12.98% on average.
| S-EPMC7054268 | biostudies-literature
Project description:Water usage is closely linked with societal goals that are both local and global in scale, such as sustainable development and economic growth. It is therefore of value, particularly for long-term planning, to understand how future sectoral water usage could evolve on a global scale at fine resolution. Additionally, future water usage could be strongly shaped by global forces, such as socioeconomic and climate change, and the multi-sector dynamic interactions those forces create. We generate a novel global gridded monthly sectoral water withdrawal and consumption dataset at 0.5° resolution for 2010-2100 for a diverse range of 75 scenarios. The scenarios are harmonized with the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios to support its usage in studies evaluating the implications of uncertain human and earth system change for future global and regional dynamics. To generate the data, we couple the Global Change Analysis Model (GCAM) with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos), and a water withdrawal downscaling model (Tethys).
| S-EPMC10090044 | biostudies-literature
Project description:Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985-2014 relative to 1955-1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi-model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30-yr period is projected to increase for 2070-2099 relative to 1985-2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. Inter-model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070-2099 and especially high for the Amazon and the Tibetan Plateau.
| S-EPMC8121137 | biostudies-literature
Project description:The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
| S-EPMC10944167 | biostudies-literature
Project description:The behavioral dynamics within a social group not only could depend on individual traits and social-experience of each member, but more importantly, emerges from inter-individual interactions over time. Herein, we first present a dataset, as well as the corresponding original video recordings, of the results of 4 behavioral tests associated with fear and aggressive response performed on 106 Japanese quail. In a second stage, birds were housed with conspecifics that performed similarly in the behavioral tests in groups of 2 females and 1 male. By continuously monitoring each bird in these small social groups, we obtained time series of social and reproductive behavior, and high-resolution locomotor time series. This approach provides the opportunity to perform precise quantification of the temporal dynamics of behavior at an individual level within different social scenarios including when an individual showing continued aggressive behaviors is present. These unique datasets and videos are publicly available in Figshare and can be used in further analysis, or for comparison with existing or future data sets or mathematical models across different taxa.
| S-EPMC6890678 | biostudies-literature
Project description:Decadal time-series derived from satellite observations are useful for discriminating crops and identifying crop succession at national and regional scales. However, use of these data for crop modeling is challenged by the presence of mixed pixels due to the coarse spatial resolution of these data, which influences model accuracy, and the scarcity of field data over the decadal period necessary to calibrate and validate the model. For this data article, cloud-free satellite "Vegetation Indices 16-Day Global 250 m" Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as the Land Parcel Information System (LPIS) vector field data, were collected throughout France for the 12-year period from 2006 to the end of 2017. A GIS workflow was developed using R software to combine the MOD13Q1 and MYD13Q1 products, and then to select "pure" MODIS pixels located within single-crop parcels over the entire period. As a result, a dataset for 21,129 reference plots (corresponding to "pure" pixels) was generated that contained a spectral time-series (red band, near-infrared band, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI)) and the associated annual crop type with an 8-day time step over the period. This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.
| S-EPMC6889487 | biostudies-literature
Project description:Because microbial plankton in the ocean comprise diverse bacteria, algae, and protists that are subject to environmental forcing on multiple spatial and temporal scales, a fundamental open question is to what extent these organisms form ecologically cohesive communities. Here we show that although all taxa undergo large, near daily fluctuations in abundance, microbial plankton are organized into clearly defined communities whose turnover is rapid and sharp. We analyze a time series of 93 consecutive days of coastal plankton using a technique that allows inference of communities as modular units of interacting taxa by determining positive and negative correlations at different temporal frequencies. This approach shows both coordinated population expansions that demarcate community boundaries and high frequency of positive and negative associations among populations within communities. Our analysis thus highlights that the environmental variability of the coastal ocean is mirrored in sharp transitions of defined but ephemeral communities of organisms.
| S-EPMC5773528 | biostudies-literature
Project description:The relationship between precipitation deficits and extreme hot temperatures has been documented in observation and modeling studies. However, it is unclear whether and how increases in maximum temperatures will impact diurnal temperature range (DTR) extremes. Here, we used observational data sets from meteorological stations in China to examine the trends in high DTR extremes from 1971 to 2013, represented by the percentage of high DTR days (%HDD) and maximum high DTR duration (MHDD), as well as their relationships with precipitation deficits over the past four decades in China. We identified both positive and negative trends in the %HDD and MHDD in China during each season, implying an inhomogeneous behavior of DTR and DTR extremes. Furthermore, we observed a significant negative relationship between precipitation deficits and the %HDD and MHDD during each season, and the relationship was strongest in the summer. The statistical analysis of this coupled behavior indicated that precipitation deficits were related to an increase in high DTR extremes, with a 22% average higher probability of the occurrence of DTR extremes after dry conditions than wet conditions in the summer. Knowledge from this study has important implications for interpreting climate anomalies.
| S-EPMC4495382 | biostudies-literature
Project description:Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences. However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. Predictions from this model are currently available for any location in Europe for the past 70 y (1950-2019), which is the period for which all climate data used as predictor variables are available. This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and long-term variability in both hydrogen and oxygen isotopic systems. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy. Predictions from our modeling framework, Piso.AI, are available at https://isotope.bot.unibas.ch/PisoAI/.
| S-EPMC8256050 | biostudies-literature