Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future

Dataset of monthly downscaled future vapor pressure projections for the conterminous USA for RCP 4.5 and RCP 8.5 compatible with NEX-DCP30

Models that simulate ecosystems at local to regional scales require relatively fine resolution climate data. Many methods exist that downscale the native resolution output from global climate models (GCM) to finer resolutions. NASA NEX-DCP30 is a statistically downscaled 30 arcsecond resolution climate dataset widely used for climate change impact studies in the conterminous USA (CONUS), but it did not include vapor pressure data which is essential for many types of models. We downscaled vapor pressure data from 28 global climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 30 arcsecond resolution for CONUS to augment the NEX-DCP30 dataset. Monthly vapor pressure values were calculated from raw GCM output for the conterminous USA from 1950 to 2100, representing RCP4.5 and RCP8.5 climate change scenarios. Vapor pressure data were then downscaled from the GCM's native spatial resolutions to 30 arcsecond using the Bias Correction-Spatial Disaggregation (BCSD) statistical downscaling method, which had been used to create the original NEX-DCP30 dataset. PRISM LT71m gridded climate data for 1970-1999 served as the reference data. The newly created downscaled vapor pressure dataset may be used in conjunction with the existing NEX-DCP30 data as input for vegetation, fire, drought, or earth system models. The data is available at the Forest Service Research Data Archive

Hot summers in the Bighorn Basin during the early Paleogene

During the early Paleogene, climate in continental interiors is thought to have been warmer and more equable than today, but estimates of seasonal temperature variations during this period are limited. Global and regional climate models of the Paleogene predict cooler temperatures for continental interiors than are implied by proxy data and predict a seasonal range of temperature that is similar to today. Here, we present a record of summer temperatures derived from carbonate clumped isotope thermometry of paleosol carbonates from Paleogene deposits in the Bighorn Basin, Wyoming (United States). Our summer temperature estimates are ∼18 °C greater than mean annual temperature estimated from analysis of fossil leaves. When coupled, these two records yield a seasonal range of temperature similar to that in the region today, with winter temperatures that are near freezing. These data are consistent with our high-resolution climate model output for the Early Eocene in the Bighorn Basin. We suggest that temperatures in continental interiors during the early Paleogene greenhouse were warmer in all seasons, but not more equable than today. If generally true, this removes one of the long-standing paradoxes in our understanding of terrestrial climate dynamics under greenhouse conditions

Overview of ICARUSA Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO2) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models