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DOE OSTI2018

WebGIobe - A cloud-based geospatial analysis framework for interacting with climate data

While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.

Sharma, Arun↗

DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Climate data for bioclimate research of Amazon Basin central region near the city of Manaus

The current research/data set deals with environmental data related to the region around the city of Manaus, Brazil, located in the Amazon Basin central region. The research problem encompasses the answers to the questions: 'What correlations can arise from the data of climate and fauna in the Amazon Basin?" "How useful is the data from instrumented stations related to that area, related to the fauna and climatic conditions?" To answer these questions, an initial data set was generated, taking into account the main pollutants with sources from NASA satellites, the Amazon Tall Tower Observatory (ATTO) experiment, and the GOAmazon project, from 2014 to 2015, along with other local data, such as the position of thermal electric power stations. It has been worth noting the data available was not coherent among the sources, due to different measuring time intervals, heights, and geographical locations. Equally important, for future bioclimatic research, a grid space of 5-km interval and a 30-minute time period was considered within the data set, covering a surface area within the boundaries of Latitude 2.00 S to 4.25 S and Longitude 59.00 to 61.25 W. The data from NASA satellites, ATTO, and GOAmazon do not have this kind of detailed grid and time mesh. To carry on, the machine learning (ML) Random Forest (RF) algorithm was selected and used, among other ML algorithms, to fill in the data into this new grid. The features considered were: data from NASA satellites and from the two projects above, covering the pollutants: ozone, NOx, particulates with diameter down to 10 nanometers, isoprene, CO, and acetonitrile. Moreover, distances and bearings to Manaus and 11 thermal power stations nearby, night and day time periods, dry and wet seasons, were also considered to compose the features. The interpolation produced by the Random Forest algorithm aimed at the pollutants: acetonitrile, ozone, NOx, Particulate N, isoprene, and CO, which are related to bioclimatic research commonly. In total, a data set was created with 51,148,800 rows, with 44 columns, 38 for the features and 6 for the targets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Xarray Climate Data Analysis Tools

xCDAT is an extension of xarray for climate data analysis on structured grids. It serves as a modern successor to the Community Data Analysis Tools (CDAT) library. Xarray is an "open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience. Xarray includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures" (source: https://xarray.dev/). The goal of xCDAT is to provide generalizable features and utilities for simple and robust analysis of climate data. xCDAT's design philosophy is focused on reducing the overhead required to accomplish certain tasks in xarray. Some key xCDAT features are inspired by or ported from the core CDAT library, while others leverage powerful libraries in the xarray ecosystem (e.g., xESMF and cf_xarray) to deliver robust APIs.

Vo, Tom↗

DOE OSTI2015

2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference Report

The climate and weather data science community met December 9–11, 2014, in Livermore, California, for the fourth annual Earth System Grid Federation (ESGF) and Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Conference, hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UVCDATremain global collaborations committed to developing a new generation of open-source software infrastructure that provides distributed access and analysis to simulated and observed data from the climate and weather communities. The tools and infrastructure created under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change. In addition, the F2F conference fosters a stronger climate and weather data science community and facilitates a stronger federated software infrastructure. The 2014 F2F conference detailed the progress of ESGF, UV-CDAT, and other community efforts over the year and sets new priorities and requirements for existing and impending national and international community projects, such as the Coupled Model Intercomparison Project Phase Six. Specifically discussed at the conference were project capabilities and enhancements needs for data distribution, analysis, visualization, hardware and network infrastructure, standards, and resources.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Taming the Wild West: Assessing Impacts-Relevant Climate Data Products (Abbreviated Report)

Impacts-relevant Earth system data refers to observational and ESM data that are downscaled, debiased, validated, and provisioned for use by decision-makers. Impacts-relevant Earth system data is essential for mitigation and adaptation planning across a variety of regions and sectors. A vast number of these data products have emerged in recent years, which has led to confusion among stakeholders and scientists as to the best product to use. With no standard evaluation protocol available for these products, the decision on which product to use was sometimes made because it was pragmatic rather than the best product to use. This project sought to develop foundational capabilities around impacts-relevant data products that would support more informed selection and application of these products. This work has been immensely successful, driving several academic publications and supported the development of a community of practice around impacts-relevant data products. Over the project’s three years we have addressed six tasks: First, the development of standard evaluation metrics for impacts-relevant climate data; second, the development of a novel suite of atmospheric river metrics; third, the development of novel metrics for precipitation feature analysis; fourth, the development of novel metrics for assessing co-variances between temperature and precipitation; fifth, the development of a dashboard for interactive examination of impacts-relevant climate data; and sixth, the establishment of a community of practice around impacts-relevant climate data that will continue beyond the conclusion of this project.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Parallel analysis tools and new visualization techniques for ultra-large climate data set

ParVis was a project funded under LAB 10-05: “Earth System Modeling: Advanced Scientific Visualization of Ultra-Large Climate Data Sets”. Argonne was the lead lab with partners at PNNL, SNL, NCAR and UC-Davis. This report covers progress from January 1st, 2013 through Dec 1st, 2014. Two previous reports covered the period from Summer, 2010, through September 2011 and October 2011 through December 2012, respectively. While the project was originally planned to end on April 30, 2013, personnel and priority changes allowed many of the institutions to continue work through FY14 using existing funds. A primary focus of ParVis was introducing parallelism to climate model analysis to greatly reduce the time-to-visualization for ultra-large climate data sets. Work in the first two years was conducted on two tracks with different time horizons: one track to provide immediate help to climate scientists already struggling to apply their analysis to existing large data sets and another focused on building a new data-parallel library and tool for climate analysis and visualization that will give the field a platform for performing analysis and visualization on ultra-large datasets for the foreseeable future. In the final 2 years of the project, we focused mostly on the new data-parallel library and associated tools for climate analysis and visualization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

A reduced order model to analytically infer atmospheric CO 2 concentration from stomatal and climate data

To address questions related to the acceleration or deceleration of the global hydrological cycle or links between the carbon and water cycles over land, reliable data for past climatic conditions based on proxies are required. In particular, the reconstruction of palaeoatmospheric CO 2 content (C a ) is needed to assist the separation of natural from anthropogenic C a variability and to explore phase relations between C a and air temperature T a time series. Both T a and C a are needed to fingerprint anthropogenic signatures in vapor pressure deficit, a major driver used to explain acceleration or deceleration phases in the global hydrological cycle. Current approaches to C a reconstruction rely on a robust inverse correlation between measured stomatal density in leaves (ν) of many plant taxa and C a . There are two methods that exploit this correlation: The first uses calibration curves obtained from extant species assumed to represent the fossil taxa, thereby restricting the suitable taxa to those existing today. The second is a hybrid eco-hydrological/physiological approach that determines C a with the aid of systems of equations based on quasi-instantaneous leaf-gas exchange theories and fossil stomatal data collected along with other measured leaf anatomical traits and parameters. In this contribution, a reduced order model (ROM) is proposed that derives C a from a single equation incorporating the aforementioned stomatal data, basic climate (e.g. temperature), estimated biochemical parameters of assimilation and isotope data. The usage of the ROM is then illustrated by applying it to isotopic and anatomical measurements from three extant species. The ROM derivation is based on a balance between the biochemical demand and atmospheric supply of CO 2 that leads to an explicit expression linking stomatal conductance to internal CO 2 concentration (C i ) and C a . The resulting expression of stomatal conductance from the carbon economy of the leaf is then equated to another expression derived from water vapor gas diffusion that includes anatomical traits. When combined with isotopic measurements for long-term C i /C a , C a can be analytically determined and is interpreted as the time-averaged C a that existed over the life-span of the leaf. Key advantages of the proposed ROM are: 1) the usage of isotopic data provides constraints on the reconstructed atmospheric CO 2 concentration from ν, 2) the analytical form of this approach permits direct links between parameter uncertainties and reconstructed C a , and 3) the time-scale mismatch between the application of instantaneous leaf-gas exchange expressions constrained with longer-term isotopic data is reconciled through averaging rules and sensitivity analysis. The latter point was rarely considered in prior reconstruction studies that combined models of leaf-gas exchange and isotopic data to reconstruct C a from ν. The proposed ROM is not without its limitations given the need to a priori assume a parameter related to the control on photosynthetic rate. Our work here further explores immanent constraints for the aforementioned photosynthetic parameter.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Analyzing the Implications of Climate Data on Plant Hardiness Zones for Green Infrastructure Planning: Case Study of Knoxville, Tennessee and Surrounding Region

Downscaled climate data for Knoxville, Tennessee and the surrounding region were used to investigate future changing Plant Hardiness Zones due to climate change. The methodology used is the same as the US Department of Agriculture (USDA), well-known for their creation of the standard Plant Hardiness Zone map used by gardeners and planners. USDA data were calculated from observed daily data for 1976–2005. The modeled climate data for the past is daily data from 1980-2005 and the future data is projected for 2025–2050. The average of all the modeled annual extreme minimums for each time period of interest was calculated. Each 1 km raster cell was placed into zone categories based on temperature, using the same criteria and categories of the USDA. The individual models vary between suggesting little change to the Plant Hardiness Zones to suggesting Knoxville moves into the next two Hardiness Zones. But overall, the models suggest moving into the next warmer Zone. USDA currently has the Knoxville area categorized as Zone 7a. None of the Zones calculated from the climate data models placed Knoxville in Zone 7a for the similar time period. The models placed Knoxville in a cooler Hardiness Zone and projected the area to increase to Zone 7. The modeled temperature data appears to be slightly cooler than the actual temperature data and this may explain the zone discrepancy. However, overall Knoxville is projected to increase to the next warmer Zone. As the modeled data has Knoxville, overall, moving from Zone 6 to Zone 7, it can be inferred that Knoxville, Tennessee may increase from their current Zone 7 to Zone 8.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Downscaled Daily 1 km Climate Data (NEX-GDDP-CMIP6) for Southeast Texas. Full ensemble of downscaled CMIP6 climate projections at 1 km daily resolution.

For the SETx-UIFL, the daily NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset climate projections were downscaled from approximately 27 km to 1 km. The SETx dataset provides very high-resolution climate data for the historical period (1950–2014) and future scenarios derived from CMIP6 global models under the four Tier 1 Shared Socioeconomic Pathways (SSPs 1.26, 2.45, 3.70, and 5.85), developed for the IPCC Sixth Assessment Report. A subset of ten NEX-GDDP-CMIP6 models was selected to represent a balance of model families, climate sensitivities, and availability across scenarios, ensuring a diverse and reliable ensemble for regional analysis. Selected models: BCC-CSM2-MR, CESM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3, FGOALS-g3, GFDL-CM4, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. Daily variables downscaled include tasmax, tasmin, tas, pr, hurs, huss, rsds, rlds, and sfcWind.

Persad, Geeta↗

DOE OSTI2016

Stride search: A general algorithm for storm detection in high-resolution climate data

This study discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclone detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 3.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 4.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 1.0 in 2017, followed by 2.0 in 2018, and 3.0 in 2021. This new release of FRED 4.0 now has 213,941 observations of 238 root traits, for a combined total of roughly 3.4 million data fields for root traits and ancillary data together. FRED 4.0 has 39.8% more root trait observations than FRED 3.0 and a 34.4% increase in unique data sources. This release of FRED 4.0 also includes significant increases in geographic regions that have long been underrepresented in global datasets, notably in the tropical low latitudes. Ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently with root trait observations. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. Despite this fine-root focus, FRED accepts data collected from roots of all sizes and contains observations of many root classes including coarse roots. Data collection will continue for the foreseeable future. The FRED4_Entire_Database_2026.csv file is the flat csv data file for FRED 4.0, and the FRED4_dd.csv file is the data dictionary of all columns available in FRED, including column IDs, column names, definitions, and unit (where applicable).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

3rd Annual Earth System Grid Federation and 3rd Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Face-to-Face Meeting Report December 2013

The climate and weather data science community gathered December 3–5, 2013, at Lawrence Livermore National Laboratory, in Livermore, California, for the third annual Earth System Grid Federation (ESGF) and Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Meeting, which was hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UV-CDAT are global collaborations designed to develop a new generation of open-source software infrastructure that provides distributed access and analysis to observed and simulated data from the climate and weather communities. The tools and infrastructure developed under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change, while the F2F meetings help to build a stronger climate and weather data science community and stronger federated software infrastructure. The 2013 F2F meeting determined requirements for existing and impending national and international community projects; enhancements needed for data distribution, analysis, and visualization infrastructure; and standards and resources needed for better collaborations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 1

To address the need for a centralized root trait database, we are compiling the Fine-Root Ecology Database (FRED) from published literature and unpublished data. FRED Version 1 (FRED 1.0) currently houses more than 70,000 observations of root traits and their associated site, vegetation, edaphic, and climatic conditions from across the globe (see image below, which shows the more than 1000 distinct locations associated with observations in FRED 1.0). Data collection is ongoing and will continue for the foreseeable future. The more than 300 root traits currently housed in FRED 1.0 are described in detail here. FRED is focused on fine roots (less than 2 mm), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model

Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature (TNARR) and downward shortwave radiation (RNARR) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPPVPM) at seven AmeriFlux crop sites, and investigated the uncertainties in GPPVPM from climate inputs as compared with eddy covariance-based GPP (GPPEC). Results showed that TNARR agreed well with in-situ measurements; RNARR, however, was positively biased. An empirical linear correction was applied to RNARR, and significantly reduced the relative error of RNARR by ~25% for crop site-years. Overall, GPPVPM calculated from the in-situ (GPPVPM(EC)), original (GPPVPM(NARR)) and adjusted NARR (GPPVPM(adjNARR)) climate data tracked the seasonality of GPPEC well, albeit with different degrees of biases. GPPVPM(EC) showed a good match with GPPEC for maize (Zea mays L.), but was slightly underestimated for soybean (Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPPVPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean). GPPVPM(adjNARR) showed a good agreement with GPPVPM(EC) for both crops due to the reduction in the bias of RNARR. The results imply that the bias of RNARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales.

AmeriFlux↗

DOE OSTI2018

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 2.

The second version of the Fine-Root Ecology Database is available for download! Download the full FRED 2.0 data set, user guidance document, map, and list of data sources here. Prior to downloading the data, please read and follow the Data Use Guidelines, and it's worth checking out some tips for using FRED before you begin your analyses. Also, see here for an updating list of corrections to FRED 2.0.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

The Risks of Contracting the Acquisition and Processing of the Nation’s Weather and Climate Data to the Private Sector

Here, we are deeply concerned with the trend toward the purchase of commercial weather and climate data by the National Oceanic and Atmospheric Administration (NOAA) and other government agencies that may follow. One major concern is for the stability of both the raw and processed forms of these datasets as commercial enterprises come and go. Also of concern is the loss of free, publicly available data, which provide the backbone for much of the non-profit research at universities, government laboratories, and other nonprofit organizations. Related to this latter point is the questionable practice of private companies gaining the rights over data collected by instruments paid for by United States taxpayers. This letter highlights recent events that prompted our concerns and provides arguments in favor of maintaining an open and free data policy, which has established the United States as the global leader in supporting weather and climate operations, applications, basic research, and education.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗