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NASA NTRS2016

Soil Moisture Active/Passive (SMAP) Radiometer Subband Calibration and Calibration Drift

The SMAP is one of four first-tier missions recommended by the US National Research Council's Committee on Earth Science and Applications from Space (Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, Space Studies Board, National Academies Press, 2007)]. The observatory was launched on Jan 31, 2015. The goal of the SMAP is to measure the global soil moisture and freeze/thaw from space. The L-band radiometer is the passive portion of the spaceborne instrument. It measures all four Stokes antenna temperatures and outputs counts. The Level 1B Brightness Temperature (L1B_TB) science algorithm converts radiometer counts to the Earths surface brightness temperature. The results are reported in the radiometer level 1B data product together with the calibrated antenna temperature (TA) and all of the corrections to the unwanted sources contribution. The calibrated L1B data product are required to satisfy the overall radiometer error budget of 1.3 K needed to meet the soil moisture requirement of 0.04 volumetric fraction uncertainty and the calibration drift requirement of no larger than 0.4 K per month.

Peng, Jinzheng↗

NASA NTRS2022

Calibration Inter-Comparison of MODIS and VIIRS Reflective Solar Bands Using Lunar Observations

Multispectral band observations from Terra and Aqua MODIS, launched in December 1999 and May 2002, respectively, and from SNPP and NOAA-20 VIIRS, launched in November 2011 and October 2017, respectively, have continuously enabled a broad range of applications and studies of the Earth system and its changes via a set of geophysical and environmental parameters. The quality of MODIS and VIIRS science and environmental data products relies strongly on the calibration accuracy and stability of individual sensors, as well as their calibration consistency, especially for the data products generated using observations from sensors across different platforms. Both MODIS and VIIRS instruments carry a similar set of on-board calibrators for their on-orbit calibration. Besides, lunar observations are regularly scheduled and implemented in support of their reflective solar bands (RSB) calibration, especially their long-term stability monitoring. In this paper, we provide an overview of MODIS and VIIRS solar and lunar calibration methodologies applied for the RSB on-orbit calibration, and describe the approach developed for their calibration inter-comparisons using lunar observations, including corrections for the effects caused by differences in the relative spectral response and adopted solar spectra between individual sensors. The MODIS and VIIRS calibration inter-comparison results derived from their regularly scheduled lunar observations are presented and discussed, including associated uncertainties and a comparison with those derived using the Earth-view targets. Also discussed are remaining challenges in lunar calibration and inter-comparison for the Earth-observing sensors, as well as on-going efforts for future improvements.

Multispectral band↗

NASA NTRS2023

Time Series of Magnetic Field Parameters Extracted from Merged Space-Weather MDI/HMI Active Region Patches as Potential Tool for Solar Flare Forecasting

Space-Weather MDI Active Region Patches (SMARPs) and Space-Weather HMI Active Region Patches (SHARPs) are two recently developed data products, which have been used for solar flare prediction studies. The present work is an effort to expand the application of SMARP and SHARP summary heliomagnetic parameters to the forecasting of solar flares. A new data product was derived by filtering, rescaling, and merging the SMARP and SHARP summary parameter data series, which were further converted into two-dimensional arrays by selecting time slices corresponding to R-value maxima, where R-value is a measure of the unsigned magnetic flux near polarity inversion lines. The resulting combined MDI-HMI time series currently span the period between April 4, 1996 and December 13, 2022, and can be extended to a more recent date, providing an opportunity to correlate and compare them with other solar activity parameters, such as the daily solar flare index, which is computed as a sum of the product of GOES X-ray flare magnitude and flare duration, for all M- and X-class flares during a day. Preliminary results demonstrate a significant overall correlation, with Pearson coefficients between 0.339 and 0.627. In addition, an oscillating pattern is seen in the daily-averaged sliding-window correlation coefficient. Time-lagged cross-correlation indicates that a leader-follower dynamic exists in some parameters, especially R-value, where they lead the flare index by at least several days, which may have potential for further application in space weather forecasting.

Heliophysics↗

NASA NTRS2008

Automated Production of Movies on a Cluster of Computers

A method of accelerating and facilitating production of video and film motion-picture products, and software and generic designs of computer hardware to implement the method, are undergoing development. The method provides for automation of most of the tedious and repetitive tasks involved in editing and otherwise processing raw digitized imagery into final motion-picture products. The method was conceived to satisfy requirements, in industrial and scientific testing, for rapid processing of multiple streams of simultaneously captured raw video imagery into documentation in the form of edited video imagery and video derived data products for technical review and analysis. In the production of such video technical documentation, unlike in production of motion-picture products for entertainment, (1) it is often necessary to produce multiple video derived data products, (2) there are usually no second chances to repeat acquisition of raw imagery, (3) it is often desired to produce final products within minutes rather than hours, days, or months, and (4) consistency and quality, rather than aesthetics, are the primary criteria for judging the products. In the present method, the workflow has both serial and parallel aspects: processing can begin before all the raw imagery has been acquired, each video stream can be subjected to different stages of processing simultaneously on different computers that may be grouped into one or more cluster(s), and the final product may consist of multiple video streams. Results of processing on different computers are shared, so that workers can collaborate effectively.

Nail, Jasper↗

NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗

NASA NTRS1991

Science Data Center concepts for moderate-sized NASA missions

The paper describes the approaches taken by the NASA Science Data Operations Center to the concepts for two future NASA moderate-sized missions, the Orbiting Solar Laboratory (OSL) and the Tropical Rainfall Measuring Mission (TRMM). The OSL space science mission will be a free-flying spacecraft with a complement of science instruments, placed in a high-inclination, sun synchronous orbit to allow continuous study of the sun for extended periods. The TRMM is planned to be a free-flying satellite for measuring tropical rainfall and its variations. Both missions will produce 'standard' data products for the benefit of their communities, and both depend upon their own scientific community to provide algorithms for generating the standard data products.

Price, R.↗

NASA NTRS2019

Terra MODIS: 20 years of on-orbit calibration and performance

For nearly 20 years, Terra MODIS observations have generated a broad range of data products, enabling the remote sensing community and users worldwide for their studies of many key geophysical parameters of the Earth’s system. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4 μm, that are calibrated by a set of on-board calibrators (OBC). Also contributed to sensor on-orbit calibration and characterization are near monthly-scheduled lunar observations and long-term trends of sensor responses over select ground targets. The quality of MODIS data products relies strongly on the dedicated efforts to the operate instrument, derive and update calibration parameters, and improve calibration strategies and algorithms in order to address on-orbit changes of sensor characteristics and its OBC functions. This paper provides an overview of Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve instrument and OBC functions and their implementation strategies. It illustrates sensor on-orbit performance using data from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms or updates of calibration look-up tables (LUTs). Also described in this paper are lessons learned from Terra MODIS and future efforts to further extend its long-term data records.

Terra↗

NASA NTRS2017

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Earth System Data Record↗

NASA NTRS2013

The Soil Moisture Active Passive (SMAP): Radar Measurements at High Latitudes and of Freeze/Thaw State

The Soil Moisture Active/Passive (SMAP) mission is scheduled for a late 2014 launch date. The mission will use both active radar and passive radiometer instruments at L-Band. In order to achieve a wide swath at sufficiently high resolution for both active and passive channels, an instrument architecture that uses a large rotating reflector is employed. In this paper, a focus will be places on the radar design and associated data products at high latitudes. The radar will employ synthetic-aperture processing to achieve a "moderate" resolution dual-pol product over a 1000 km swath. Because the radar is operating continuously, very frequent temporal coverage will be achieved at high latitudes. This data will be used, among other things, to produce a surface freeze/thaw state data product.

Earth observing↗

NASA NTRS2019

The Terrestrial Organism and Biogeochemistry Spatial Sampling Design for the National Ecological Observatory Network

The National Ecological Observatory Network (NEON) seeks to facilitate ecological prediction at a continental scale by measuring processes that drive change and responses at sites across the United States for thirty years. The spatial distribution of observations of terrestrial organisms and soil within NEON sites is determined according to a “design‐based” sample design that relies on the randomization of sampling locations. Development of the sample design was guided by high‐level NEON objectives and the multitude of data products that will be subjected to numerous analytical approaches to address the causes and consequences of ecological change. A requirement framework permeates the NEON design, ensuring traceability from each facet of the design to the high‐level requirements that make the NEON mission statement actionable. Requirements were developed for the terrestrial sample design to guide the key components of the design: Randomizing the sample locations ensures the unbiased collection of data, is appropriate for organisms and soil, and provides data suitable for a variety of analyses. Stratification increases efficiency and allows sampling to focus on those parts of the landscape measured by other NEON observation platforms. Attention to the sample size and spatial plot allocation ensures that data products will be sufficient to inform questions asked of the data and the NEON objectives. Establishing a framework with the capacity for re‐evaluate and design iteration allows for adaption to unexpected challenges and optimization of the sample design based on early data returns. The utility of the NEON sampling design is highlighted by its application across terrestrial systems. The data generated from this unique design will be used to quantify patterns in: the abundance and diversity of small mammals, breeding birds, insects, and soil microbes; vegetation structure, biomass, productivity, and diversity; and soil biogeochemistry.

National Ecological Observatory Network↗

NASA NTRS2007

A Contrast in Use of Metrics in Earth Science Data Systems

In recent years there has been a surge in the number of systems for processing, archiving and distributing remotely sensed data. Such systems, working independently as well as in collaboration, have been contributing greatly to the advances in the scientific understanding of the Earth system, as well as utilization of the data for nationally and internationally important applications. Among such systems, we consider those that are developed by or under the sponsorship of NASA to fulfill one of its strategic objectives: "Study Earth from space to advance scientific understanding and meet societal needs." NASA's Earth science data systems are of varying size and complexity depending on the requirements they are intended to meet. Some data systems are regarded as NASA's "Core Capabilities" that provide the basic infrastructure for processing, archiving and distributing a set of data products to a large and diverse user community in a robust and reliable manner. Other data systems constitute "Community Capabilities". These provide specialized and innovative services to data users and/or research products offering new scientific insight. Such data systems are generally supported by NASA through peer reviewed competition. Examples of Core Capabilities are 1. Earth Observing Data and Information System (EOSDIS) with its Distributed Active Archive Centers (DAACs), Science Investigator-led Processing Systems (SIPSs), and the EOS Clearing House (ECHO); 2. Tropical Rainfall Measurement Mission (TRMM) Science Data and Information System (TSDIS); 3. Ocean Data Processing System (ODPS); and 4. CloudSat Data Processing Center. Examples of Community Capabilities are projects under the Research, Education and Applications Solutions Network (REASON), and Advancing Collaborative Connections for Earth System Science (ACCESS) Programs. In managing these data system capabilities, it is necessary to have well-established goals and to measure progress relative to them. Progress is measured through "metrics", which can be a combination of quantitative as well as qualitative assessments. The specific metrics of interest depend on the user of the metrics as well as the type of data system. The users of metrics can be data system managers, program managers, funding agency or the public. Data system managers need metrics for assessing and improving the performance of the system and for future planning. Program managers need metrics to assess progress and the value of the data systems sponsored by them. Also, there is a difference in the metrics needed for core capabilities that tend to be more complex, larger and longer-term compared to community capabilities and the community capabilities that tend to be simpler, smaller and shorter-term. Even among community capabilities there are differences; hence the same set of metrics does not apply to all. Some provide data products to users, some provide services that enable better utilization of data or interoperability among other systems, and some are a part of a larger project where provision of data or services is only a minor activity. There is also a contrast between metrics used for internal and external purposes. Examples of internal purposes are: ensuring that the system meets its requirements, and planning for evolution and growth. Examples of external purposes are: providing to sponsors indicators of success of the systems, demonstrating the contributions of the system to overall program success, etc. This paper will consider EOSDIS, REASON and ACCESS programs to show the various types of metrics needed and how they need to be tailored to the types of data systems while maintaining the overall management goals of measuring progress and contributions made by the data systems.

Ramapriyan, Hampapuram↗

DOE OSTI2021

Assessing data change in scientific datasets

Summary Scientific datasets are growing rapidly and becoming critical to next‐generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance. Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad‐hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real‐time (online) and offline analyses. We employ a variety of fast‐to‐compute numerical analyses, graphical data change representations, and more resource‐intensive recomputations of a subset of the data product. We illustrate the application of our approach using three scientific diverse use cases, namely, satellite, cosmological, and x‐ray data. The results show that a variety of data change metrics should be employed to enable a comprehensive representation and qualitative evaluation of data changes.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Lessons Learned While Exploring Cloud-Native Architectures for NASA EOSDIS Applications and Systems

NASA's Earth Observing System (EOS) is a coordinated series of satellites for long term global observations. NASA's Earth Observing System Data and Information System (EOSDIS) is a multi-petabyte-scale archive of environmental data that supports global climate change research by providing end-to-end services from EOS instrument data collection to science data processing to full access to EOS and other earth science data. On a daily basis, the EOSDIS ingests, processes, archives and distributes over 3 terabytes of data from NASA's Earth Science missions representing over 6000 data products ranging from various types of science disciplines. EOSDIS has continually evolved to improve the discoverability, accessibility, and usability of high-impact NASA data spanning the multi-petabyte-scale archive of Earth science data products. Reviewed and approved by Chris Lynnes.

Cumulus↗

NASA NTRS2014

File Specification for the 7-km GEOS-5 Nature Run, Ganymed Release Non-Hydrostatic 7-km Global Mesoscale Simulation

This document describes the gridded output files produced by a two-year global, non-hydrostatic mesoscale simulation for the period 2005-2006 produced with the non-hydrostatic version of GEOS-5 Atmospheric Global Climate Model (AGCM). In addition to standard meteorological parameters (wind, temperature, moisture, surface pressure), this simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), O3, CO and CO2. This model simulation is driven by prescribed sea-surface temperature and sea-ice, daily volcanic and biomass burning emissions, as well as high-resolution inventories of anthropogenic sources. A description of the GEOS-5 model configuration used for this simulation can be found in Putman et al. (2014). The simulation is performed at a horizontal resolution of 7 km using a cubed-sphere horizontal grid with 72 vertical levels, extending up to to 0.01 hPa (approximately 80 km). For user convenience, all data products are generated on two logically rectangular longitude-latitude grids: a full-resolution 0.0625 deg grid that approximately matches the native cubed-sphere resolution, and another 0.5 deg reduced-resolution grid. The majority of the full-resolution data products are instantaneous with some fields being time-averaged. The reduced-resolution datasets are mostly time-averaged, with some fields being instantaneous. Hourly data intervals are used for the reduced-resolution datasets, while 30-minute intervals are used for the full-resolution products. All full-resolution output is on the model's native 72-layer hybrid sigma-pressure vertical grid, while the reduced-resolution output is given on native vertical levels and on 48 pressure surfaces extending up to 0.02 hPa. Section 4 presents additional details on horizontal and vertical grids. Information of the model surface representation can be found in Appendix B. The GEOS-5 product is organized into file collections that are described in detail in Appendix C. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GEOS-5 Nature Run portal: http://gmao.gsfc.nasa.gov/projects/G5NR. Information on the scientific quality of this simulation will appear in a forthcoming NASA Technical Report Series on Global Modeling and Data Assimilation to be available from http://gmao.gsfc.nasa.gov/pubs/tm/.

GEOS-5↗

NASA NTRS2006

Development and Implementation of a Comprehensive Radiometric Validation Protocol for the CERES Earth Radiation Budget Climate Record Sensors

The CERES Flight Models 1 through 4 instruments were launched aboard NASA's Earth Observing System (EOS) Terra and Aqua Spacecraft into 705 Km sun-synchronous orbits with 10:30 a.m. and 1:30 p.m. equatorial crossing times. These instruments supplement measurements made by the CERES Proto Flight Model (PFM) instrument launched aboard NASA's Tropical Rainfall Measuring Mission (TRMM) into a 350 Km, 38-degree mid-inclined orbit. CERES Climate Data Records consist of geolocated and calibrated instantaneous filtered and unfiltered radiances through temporally and spatially averaged TOA, Surface and Atmospheric fluxes. CERES filtered radiance measurements cover three spectral bands including shortwave (0.3 to 5 microns), total (0.3 to 100 microns) and an atmospheric window channel (8 to 12 microns). The CERES Earth Radiation Budget measurements represent a new era in radiation climate data, realizing a factor of 2 to 4 improvement in calibration accuracy and stability over the previous ERBE climate records, while striving for the next goal of 0.3-percent per decade absolute stability. The current improvement is derived from two sources: the incorporation of lessons learned from the ERBE mission in the design of the CERES instruments and the development of a rigorous and comprehensive radiometric validation protocol consisting of individual studies covering different spatial, spectral and temporal time scales on data collected both pre and post launch. Once this ensemble of individual perspectives is collected and organized, a cohesive and highly rigorous picture of the overall end-to-end performance of the CERES instrument's and data processing algorithms may be clearly established. This approach has resulted in unprecedented levels of accuracy for radiation budget instruments and data products with calibration stability of better than 0.2-percent and calibration traceability from ground to flight of 0.25-percent. The current work summarizes the development, philosophy and implementation of the protocol designed to rigorously quantify the quality of the data products as well as the level of agreement between the CERES TRMM, Terra and Aqua climate data records.

Priestley, K. J.↗

NASA NTRS2015

Aerosol Correction for Improving OMPS/LP Ozone Retrieval

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.

OMPS/LP↗

NASA NTRS2003

Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, Using Land Surface Climatology and Autoregressive Analysis

There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

Grass, David↗

DOE OSTI2022

Global Carbon Budget 2022

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

54 ENVIRONMENTAL SCIENCES↗