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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Quantifying spatially and temporally explicit CO 2 fertilization effects on global terrestrial ecosystem carbon dynamics

Abstract Current terrestrial ecosystem models are usually driven with global average annual atmospheric carbon dioxide ( CO 2 ) concentration data at the global scale. However, high‐precision CO 2 measurement from eddy flux towers showed that seasonal, spatial surface atmospheric CO 2 concentration differences were as large as 35 ppmv and the site‐level tests indicated that the CO 2 variation exhibited different effects on plant photosynthesis. Here we used a process‐based ecosystem model driven with two spatially and temporally explicit CO 2 data sets to analyze the atmospheric CO 2 fertilization effects on the global carbon dynamics of terrestrial ecosystems from 2003 to 2010. Our results demonstrated that CO 2 seasonal variation had a negative effect on plant carbon assimilation, while CO 2 spatial variation exhibited a positive impact. When both CO 2 seasonal and spatial effects were considered, global gross primary production and net ecosystem production were 1.7 Pg C·yr −1 and 0.08 Pg C·yr −1 higher than the simulation using uniformly distributed CO 2 data set and the difference was significant in tropical and temperate evergreen broadleaf forest regions. This study suggests that the CO 2 observation network should be expanded so that the realistic CO 2 variation can be incorporated into the land surface models to adequately account for CO 2 fertilization effects on global terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Deducing Land-Atmosphere Coupling Regimes from SMAP Soil Moisture

In recent years, there has been a growing recognition of the significance of Land-Atmosphere (L-A) interactions and feedback mechanisms in understanding and predicting Earth’s water and energy cycles. Soil moisture plays a critical role in mediating the strength of L-A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L-A coupling strength within the Convective Triggering Potential (CTP) and Humidity Index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using Triple Collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally as compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L-A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over two decades (2003-2022) where L-A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP-HI underscores the importance of soil moisture data in estimating coupling strength within the CTP-HI framework. These findings lay the groundwork for understanding the role of L-A interactions and drought evolution due to soil moisture variations, by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Land-atmosphere coupling↗
DOE OSTI2025

A framework for testing soil carbon dynamics post land-use transition in a multisector dynamics model

Soil carbon plays a crucial role in the global carbon cycle. Changes in land use can determine whether carbon is stored or is emitted into the atmosphere as carbon dioxide, which has broad implications for the human and Earth systems. These feedbacks to the carbon cycle and their socio-economic drivers are modelled by many global multisector dynamics models to project future possibilities for the human-Earth system. One notable model of this class is the Global Change Analysis Model (GCAM), which uses a simplified process to model soil organic carbon (SOC) content after land-use transition across 384 land units. While the current GCAM soil carbon framework is based on scientific principles, it has not been tested against experimental data. This work examines rates of SOC change from GCAM input data. Specifically, first order rate constants derived from model inputs were compared to values from two syntheses to assess GCAM’s accuracy. Welch’s t-tests and linear models were used to determine if rate constants were consistent across all tested geographical areas and land-use transition types. While we found that there was general agreement on the direction and magnitude (i.e., rate) of SOC change, the rate constant derived from GCAM and empirical values differed strongly in a subset of specific instances. These results indicate that GCAM’s current SOC dynamics during land use transition successfully capture broad patterns of change in this critical carbon pool, but should be interpreted with caution at finer spatial scales. One potential cause of these discrepancies is our highly aggregated variable, soil timescale, which could be made more granular to improve accuracy. When using economically rooted multisector dynamics models, such as GCAM, it is critical to understand such model limitations for representing specific Earth system processes.

carbon↗
NASA NTRS2018

Global-scale Evaluation of SMAP, SMOS and ASCAT Soil Moisture Products Using Triple Collocation

Global-scale surface soil moisture products are currently available from multiple remote sensing platforms. Footprint-scale assessments of these products are generally restricted to limited number of densely-instrumented validation sites. However, by taking active and passive soil moisture products together with a third independent soil moisture estimates via land surface modeling, triple collocation (TC) can be applied to estimate the correlation metric of satellite soil moisture products (versus an unknown ground truth) over a quasi-global domain. Here, an assessment of Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS) and Advanced SCATterometer (ASCAT) surface soil moisture retrievals via TC is presented. Considering the potential violation of TC error assumptions, the impact of active-passive and satellite-model error cross correlations on the TC-derived inter-comparison results is examined at in situ sites using quadruple collocation analysis. In addition, confidence intervals for the TC-estimated correlation metric are constructed from moving-block bootstrap sampling designed to preserve the temporal persistence of the original (unevenly-sampled) soil moisture time-series. This study is the first to apply TC to obtain a robust global-scale cross-assessment of SMAP, SMOS and ASCAT soil moisture retrieval accuracy in terms of anomaly temporal correlation. Our results confirm the overall advantage of SMAP (with a global average anomaly correlation of 0.76) over SMOS (0.66) and ASCAT (0.63) that has been established in several recent regional, ground-based studies. SMAP is also the best-performing product over the majority of applicable land pixels (52%), although SMOS and ASCAT each shows advantage in distinct geographic regions.

global-scale↗
DOE OSTI2020

Feedbacks Between Surface Deformation and Permafrost Degradation in Ice Wedge Polygons, Arctic Coastal Plain, Alaska

In the past three decades, an abrupt, pan-Arctic acceleration of ice wedge melting has transformed tundra landscapes, spurring the formation of hummock-like features known as high-centered polygons (HCPs). This rapid geomorphic transition profoundly alters regional hydrology and influences surface emissions of CO 2 and CH 4 . In Arctic Alaska, most recent instances of ice wedge degradation have arrested within 15–20 years of inception, stabilizing HCP microtopography. However, feedbacks between ground surface deformation and permafrost stability are incompletely understood, limiting our capacity to predict trajectories of landscape evolution in a still warmer future. In this work, we use field data from a site near Prudhoe Bay, Alaska, to develop a modeling-based framework for assessing the strength of positive (i.e., exacerbating) feedbacks on ice wedge degradation, focusing on the importance of heterogeneity in surface drainage and microtopographic conditions. Our simulations suggest that, when troughs are narrow, positive feedbacks on ice wedge melting (associated with thermokarst pool formation) are relatively weak. Positive feedbacks are markedly stronger beneath wide troughs, such as those that form above older, larger ice wedges. Seasonal thaw abruptly accelerates once a talik begins to form beneath wide and deep thermokarst pools. Once a talik initiates, winter severity and snowpack thickness increase in importance as predictors of thaw intensity in summer. Our results indicate that meter-scale heterogeneity in polygonal microtopography potentially exerts strong, nonlinear controls on thermokarst trajectories. These findings are useful for predicting future thermokarst dynamics and for interpreting the results from coarser-resolution land surface models operating at greater spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Evaluating Lunar Descent and Landing Performance From a Near Rectilinear Halo Orbit Using Linear Covariance Resetting Techniques

Upcoming lunar programs are striving the achieve precision landing in a safe and robust manner. Various elements impact this mission objective ranging from on-orbit operations with ground station tracking to incorporating relative sensors with hazard detection and avoidance (HDA) to support the final approach and landing phase. Modeling the impacts of ground tracking, trajectory replanning, relative navigation sensors, and particularly a potential HDA system on the integrated closed-loop GN\&C system performance poses a unique challenge due to the complexity and interaction with multiple facets of the vehicle including the trajectory design, sensing hardware, navigation system, guidance and targeting, and the overall mission concept of operations. This paper outlines techniques to systematically analyze and compare the performance impacts of ground tracking and replanning and an HDA system where the onboard navigation errors are reset or uploaded from an external source and the vehicle's reference trajectory is regenerated requiring the system dispersions to also be reset to reflect this in-flight profile adjustment. To illustrate the application of these general techniques for analyzing the performance impacts due to incorporating these resetting events, they are demonstrated with a human lunar descent and landing scenario starting from a near rectilinear halo orbit (NRHO) until the vehicle precisely reaches its predetermined landing site on the lunar surface. Performance metrics such as inertial and relative navigation errors, trajectory dispersions, footprint dispersions, and propellant usage are provided.

GN&C↗
DOE OSTI2023

Evaluation of Multi-Fidelity Soil Moisture Products Across the Continental United States

We have aggregated the most recent soil moisture datasets from a diverse range of sources, encompassing the Continental United States (CONUS). These sources encompass gridded data from remote sensing products, reanalysis products, machine learning-based projects, and land surface modeling products. Additionally, we have obtained and processed in-situ soil moisture observations from the International Soil Moisture Network. The collected datasets exhibit variations in both temporal and spatial resolutions. Among the 20 datasets, six are available at a spatial resolution of 0.25 degrees, while three are at a coarser spatial resolution of 25 km. To minimize spatial interpolation, we conducted data uncertainty evaluations at the 0.25-degree spatial resolution. For our data evaluations, we maintained a monthly temporal resolution, which effectively captures soil moisture seasonality and interannual variability. Our data processing strategy preserves the raw data and interpolated data at their original temporal resolutions. Datasets with higher temporal resolutions, including daily, three-hourly, and hourly datasets, are set aside for subsequent analyses. These analyses will delve into topics such as soil moisture changes and recovery during extreme weather events. Furthermore, we have processed auxiliary data to enhance our evaluation, leveraging tools such as Google Earth Engine. This includes incorporating topography data, land use land cover data, Köppen-Geiger climate classification, and more to provide a comprehensive assessment from multiple sources.

Li, Lingcheng↗
NASA NTRS2009

Watershed and Hydrodynamic Modeling for Evaluating the Impact of Land Use Change on Submerged Aquatic Vegetation and Seagrasses in Mobile Bay

There is a continued need to understand how human activities along the northern Gulf of Mexico coast are impacting the natural ecosystems. The gulf coast is experiencing rapid population growth and associated land cover/land use change. Mobile Bay, AL is a designated pilot region of the Gulf of Mexico Alliance (GOMA) and is the focus area of many current NASA and NOAA studies, for example. This is a critical region, both ecologically and economically to the entire United States because it has the fourth largest freshwater inflow in the continental USA, is a vital nursery habitat for commercially and recreational important fisheries, and houses a working waterfront and port that is expanding. Watershed and hydrodynamic modeling has been performed for Mobile Bay to evaluate the impact of land use change in Mobile and Baldwin counties on the aquatic ecosystem. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for land use Scenarios in 1948, 1992, 2001, and 2030. The Prescott Spatial Growth Model was used to project the 2030 land use scenario based on observed trends. All land use scenarios were developed to a common land classification system developed by merging the 1992 and 2001 National Land Cover Data (NLCD). The LSPC model output provides changes in flow, temperature, sediments and general water quality for 22 discharge points into the Bay. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment concentrations on a grid with four vertical profiles throughout the Bay s aquatic ecosystems. The models were calibrated using in-situ data collected at sampling stations in and around Mobile bay. This phase of the project has focused on sediment modeling because of its significant influence on light attenuation which is a critical factor in the health of submerged aquatic vegetation. The impact of land use change on sediment concentrations was evaluated by analyzing the LSPC and EFDC sediment simulations for the four land use scenarios. Such analysis was also performed for storm and non-storm periods. In- situ data of total suspended sediments (TSS) and light attenuation were used to develop a regression model to estimate light attenuation from TSS. This regression model was used to derive marine light attenuation estimates throughout Mobile bay using the EFDC TSS outputs. The changes in sediment concentrations and associated impact on light attenuation in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on seagreasses and Submerged Aquatic Vegetation (SAV) habitat. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of sediments due to land use driven flow changes with the restoration potential of SAVs.

Estes, Maurice G.↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Validation of Surface Skin Temperature and Moisture Profiles Using Satellite Data

New validation techniques and metrics using satellite data have been developed to evaluate the quality of model-based estimates of surface skin temperature (Tg) and moisture profiles (q). The satellite data consist of clear sky outgoing long-wave radiation (CLR), broadband radiances from 8 to 12 mu (RadWn), brightness temperature centered around 10.8 mu (Tbb), and total precipitable water (TPW) from microwave radiometry. We show that CLR can be used to diagnose Tg. Furthermore, by using a combination of CLR and RadWn from CERES-TRMM measurements and TPW from SSM/I, we are able to identify errors in the moisture profile. Finally, three-hourly Tbb from the International Satellite Cloud Climatology Project can be used to evaluate the amplitude and diurnal variation of Tg. For purpose of illustration, Tg and q are evaluated from runs with an early version of the Goddard Earth Observing System Data Assimilation System (GEOS-2). It is found that, in general, Tg is too cold in the winter hemisphere and q is too wet in the upper atmosphere. In order to address these deficiencies, several improvements have been implemented into GEOS-2, including a Land-Surface-Model, a Moist Turbulence Scheme, and the assimilation of new TOVS retrievals. Preliminary results indicate positive impacts from each of these implementations.

Wu, Man Li C.↗
NASA NTRS1992

Chapman Conference on the Hydrologic Aspects of Global Climate Change, Lake Chelan, WA, June 12-14, 1990, Selected Papers

The present conference on the hydrological aspects of global climate change discusses land-surface schemes for future climate models, modeling of the land-surface boundary in climate models as a composite of independent vegetation, a land-surface hydrology parameterizaton with subgrid variability for general circulation models, and conceptual aspects of a statistical-dynamical approach to represent landscape subgrid-scale heterogeneities in atmospheric models. Attention is given to the impact of global warming on river runoff, the influence of atmospheric moisture transport on the fresh water balance of the Atlantic drainage basin, a comparison of observations and model simulations of tropospheric water vapor, and the use of weather types to disaggregate the prediction of general circulation models. Topics addressed include the potential response of an Arctic watershed during a period of global warming and the sensitivity of groundwater recharge estimates to climate variability and change.

Lettenmaier, Dennis P.↗
NASA NTRS2011

Estimating Evapotranspiration Using an Observation Based Terrestrial Water Budget

Evapotranspiration (ET) is difficult to measure at the scales of climate models and climate variability. While satellite retrieval algorithms do exist, their accuracy is limited by the sparseness of in situ observations available for calibration and validation, which themselves may be unrepresentative of 500m and larger scale satellite footprints and grid pixels. Here, we use a combination of satellite and ground-based observations to close the water budgets of seven continental scale river basins (Mackenzie, Fraser, Nelson, Mississippi, Tocantins, Danube, and Ubangi), estimating mean ET as a residual. For any river basin, ET must equal total precipitation minus net runoff minus the change in total terrestrial water storage (TWS), in order for mass to be conserved. We make use of precipitation from two global observation-based products, archived runoff data, and TWS changes from the Gravity Recovery and Climate Experiment satellite mission. We demonstrate that while uncertainty in the water budget-based estimates of monthly ET is often too large for those estimates to be useful, the uncertainty in the mean annual cycle is small enough that it is practical for evaluating other ET products. Here, we evaluate five land surface model simulations, two operational atmospheric analyses, and a recent global reanalysis product based on our results. An important outcome is that the water budget-based ET time series in two tropical river basins, one in Brazil and the other in central Africa, exhibit a weak annual cycle, which may help to resolve debate about the strength of the annual cycle of ET in such regions and how ET is constrained throughout the year. The methods described will be useful for water and energy budget studies, weather and climate model assessments, and satellite-based ET retrieval optimization.

Rodell, Matthew↗
DOE OSTI2018

Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States

Fine particulate matter (PM2.5) from U.S. anthropogenic sources is decreasing. However, previous studies have predicted that PM2.5 emissions from wildfires will increase in the midcentury to next century, potentially offsetting improvements gained by continued reductions in anthropogenic emissions. Therefore, some regions could experience worse air quality, degraded visibility, and increases in population-level exposure. We use global climate model simulations to estimate the impacts of changing fire emissions on air quality, visibility, and premature deaths in the middle and late 21st century. We find that PM2.5 concentrations will decrease overall in the contiguous United States (CONUS) due to decreasing anthropogenic emissions (total PM2.5 decreases by 3% in Representative Concentration Pathway [RCP] 8.5 and 34% in RCP4.5 by 2100), but increasing fire-related PM2.5 (fire-related PM2.5 increases by 55% in RCP4.5 and 190% in RCP8.5 by 2100) offsets these benefits and causes increases in total PM2.5 in some regions. We predict that the average visibility will improve across the CONUS, but fire-related PM2.5 will reduce visibility on the worst days in western and southeastern U.S. regions. We estimate that the number of deaths attributable to total PM2.5 will decrease in both the RCP4.5 and RCP8.5 scenarios (from 6% to 4–5%), but the absolute number of premature deaths attributable to fire-related PM2.5 will double compared to early 21st century. We provide the first estimates of future smoke health and visibility impacts using a prognostic land-fire model. Our results suggest the importance of using realistic fire emissions in future air quality projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO 2 emissions during the 1959–2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

58 GEOSCIENCES↗
DOE OSTI2019

Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas

This study defines albedo and reflectance in the context of solar applications. It examines the main sources of surface albedo data that can be used to help solar irradiance modeling efforts, such as undertaken by the National Renewable Energy Laboratory (NREL) over the Americas, for instance, per the National Solar Radiation Database (NSRDB). The MODIS-based albedo database, as used as input to derive historical time series of solar irradiance in the current version of the NSRDB, is compared to other sources of global gridded data over the Americas in general and some specific areas in particular. The particular role of snow cover is analyzed. It is found that, in the current MODIS-based NSRDB implementation, snow-impacted areas have been assigned a too high albedo in general because of the lack of consideration for the antagonistic effect of surface roughness, among other factors. Other albedo databases, such as CMSAF, MINES-ParisTech or the ERA5 reanalysis, do not display such high albedos at latitudes above ~40 degrees N. The gap-free and snow-free MODIS MCD43GF product is found affected by a long-term downward trend over some areas and out-of-phase seasonal effects over other areas, in comparison with MERRA-2 reanalysis data in particular. Comparisons with actual albedo measurements at the arid Desert Rock site in Nevada also show that albedo data derived from land surface models are affected by systematic bias. Five albedo databases are intercompared for the area around Ivanpah Playa, California, which has great importance in both remote sensing and solar applications. Various aspects of the proper usage of albedo data in solar radiation modeling are discussed, regarding calculations of both the backscattering process and reflections on a tilted surface: spatial resolution, temporal resolution, angular effects, and spectral effects. An important distinction is made between the use of albedo data at ~4 km resolution for backscattering calculations and at ~10 m resolution for transposition calculations to tilted planes, which might additionally require spectral information for monofacial or bifacial PV applications. It is shown that the tilted calculations usually performed in solar resource assessments are far from realistic in the case of most large-scale PV applications, for which a more suitable methodological approach is described. For such needs, the limitations of the current sources of high-resolution reflectance data are discussed. The impact of surface albedo estimation errors on the estimation of backscattered irradiance over snowy areas is evaluated. It is found non-negligible, and even large under cloudy conditions.

14 SOLAR ENERGY↗
DOE OSTI2021

A data-driven global soil heterotrophic respiration dataset and the drivers of its inter-annual variability

Soil heterotrophic respiration (SHR), one of the primary carbon fluxes from terrestrial ecosystems to the atmosphere, is important for carbon-climate feedbacks because of its sensitivity to available litter and soil carbon, climatic conditions, and nutrient availability. However, until recently limited SHR data were available, and most published global SHR estimates have either a short time span, coarse spatial resolution, or reply on overly-simple model formulations. To better understand and quantify the global distribution of SHR and its sensitivity to climate variability, we produced a new global SHR dataset using Random Forest algorithms, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity as explanatory variables. We estimated a global total SHR of 46.8 Pg C yr-1 over 1985-2013 (95% confidence interval: 38.6-56.3 Pg C yr-1), with a significant increasing trend of 0.03 Pg C yr-2 during this period. We found that the choice of soil moisture datasets contributes more to the difference among these data-driven SHR members rather than that of productivity, temperature and precipitation data sources. We also analyzed the influence of climatic variables on the inter-annual variability (IAV) of our SHR product. Water availability was the dominant driver of IAV at global scales, although the inferred sensitivity depends on the choice of the soil moisture gridded dataset. At the ecosystem scale, temperature strongly controls the IAV of SHR in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning gridded SHR dataset and outputs from process-based land surface models (TRENDYv6) show agreement for a strong association between water variability and SHR IAV at the global scale, but the two approaches lead to different temporal trend globally and different controlling variables for IAV at the ecosystem scale. Our study provides evidence for the pervasive and important role of water availability in driving SHR, indicating both a direct effect limiting decomposition rates and an indirect effect through the amount of fresh organic matter made available to SHR from productivity. In consideration of potential limitations and uncertainties remaining in our data-driven SHR datasets, we call for a more scientifically designed observation network for SHR, more observation data compilation, and increased use of deep learning methods making maximum use of observation data in hand. This will benefit process-based models, and improve our understanding of SHR response to future anomalous environmental conditions.

Yao, Yitong↗
NASA NTRS2021

Improved Estimates of Pentad Precipitation through the Merging of Independent Precipitation Datasets

Three independent, quasi-global, gridded datasets of precipitation (a rain gauge-based dataset, the satellite-only component of the NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission [IMERG] Final Run precipitation product, and precipitation estimates derived from NASA Soil Moisture Active Passive [SMAP] soil moisture retrievals), are objectively combined into a single pentad precipitation dataset at 36-km resolution using a unique approach based on extended triple collocation. The quality of each of the four datasets is then evaluated against independent observations. When a global land surface model at 36-km resolution is integrated four times, once utilizing the merged precipitation forcing and once with each of the three contributing datasets, the near-surface soil moisture variations produced with the merged forcing validate best against independent satellite-based soil moisture fields. In addition, the merged dataset is found to be more consistent, relative to each contributor, with estimates of air temperature variations across the globe. The merged dataset thus appears to draw successfully on the complementary strengths of each contributor: the particularly high quality of the rain gauge-based dataset in areas of high gauge density, the more uniform accuracy across the globe of the IMERG data, and the moderate accuracy, particularly in semi-arid regions, of the soil moisture retrieval-based data. Plain Language Summary Obtaining measurements of precipitation across the globe can be challenging. Rain gauges in some ways provide the most accurate measurements, but gauges are absent in many parts of the world, and even where they exist, they only measure precipitation at the gauge itself and therefore may not provide an accurate large-scale average. Satellite-based estimates of precipitation largely overcome these problems, but such data have their own issues, notably a “snapshot” (rather than a time-average) character of the measurements and difficulty associated with interpreting the measured radiances in the presence of complex land surfaces. In the present paper, we use a novel approach to generate a “merged” dataset, one that optimally combines the gauge precipitation information and the satellite-based precipitation information with a third set of estimates derived from soil moisture retrievals. The merged precipitation dataset and each of the three contributors (aggregated here to 5-day averages at a spatial resolution of about 36-km) are then evaluated for consistency with independent geophysical fields. The merged dataset is found to perform best, a clear indication that it takes proper advantage of the complementary strengths of each contributor and, accordingly, that the presented approach for merging the different contributors is indeed viable.

Precipitation↗