Assessment of Dust Source Attribution to the Global Land and Ocean Regions
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Dongchul Kim↗
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VotE-Dams: a compilation of global dams' locations and attributes (v1)
This dataset represents a compilation of two global and three USA-specific datasets of dam locations and their attributes. The major hurdle toward developing this compilation was the identification of duplicates within the source datasets, especially given the variable precision of dam location coordinates. The most immediately-useful product in this dataset is a spreadsheet (VotE-Dams_v1.csv) that documents the unique dams found across the datasets, their coordinates, and their ids within the respective source datasets. We do not reproduce the source datasets (GRaND, GOODD, GeoDAR, NID, and EHA) here, but their download locations are provided in the README files ('Overview' tab). Some of the source datasets are provided as shapefiles, which require geospatial data software to open (e.g. QGIS/ArcGIS for graphical display, geopandas for Python, rgdal for R, many others freely available). The provided README documents metadata of the source datasets and provides attribute-linking information (i.e. matches attributes among various source datasets that contain the same, or similar, information but have different names). Note that the README is provided as both .xslx and a collection of .csvs (one per tab in the .xslx file). We suggest using the .xlsx version that preserves images, formatting, and sheets. .xlsx files can be viewed using (free) Google Docs or Microsoft Excel.Finally, we provide Technical Documentation.pdf that describes the procedures used to identify unique and duplicate dams.The title of this dataset refers to our 'Veins of the Earth' (VotE) project, which seeks to provide a flexible, scale-free representation of the Earth's river networks. Dams are a critical component of VotE as they heavily influence flows throughout river networks.
54 ENVIRONMENTAL SCIENCES↗
Where Dust Comes from: Global Assessment of Dust Source Attributions with AeroCom Models
The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long-range transport. Recently, a multi-model experiment, namely the AeroCom-III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East- and West-Africa, Middle East, Central- and East-Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodélé and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter-model differences are significant with the global model dust diversity in 30%–50%, but the differences in regional and seasonal scales are even larger. The multi-model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models.
Dust source attribution, Aerosol, Model↗
Unlocking the Origins of Ultracool Dwarf Radio Emission
Empirical trends in stellar X-ray and radio luminosities suggest that low-mass ultracool dwarfs (UCDs) should not produce significant radio emission. Defying these expectations, strong nonthermal emission has been observed in a few UCDs in the 1–10 GHz range, with a variable component often attributed to global aurorae and a steady component attributed to other processes, such as gyrosynchrotron emission. While both auroral and gyrosynchrotron emission peak near the critical frequency, only the latter radiation is expected to extend into millimeter wavelengths. We present Atacama Large Millimetre/Submillimeter Array (ALMA) 97.5 GHz and Very Large Array 33 GHz observations of a small survey of 5 UCDs. LP 349-25, LSR J1835+3259, and NLTT 33370 were detected at 97.5 GHz, while LP 423-31 and LP 415-20 resulted in nondetections at 33 GHz. A significant flare was observed in NLTT 33370, which reached a peak flux of 4880 ± 360 μJy, exceeding the quiescent flux by nearly an order of magnitude and lasting 20 s. These ALMA observations show bright 97.5 GHz emission with spectral indices ranging from α = 0.76 to α = −0.29, suggestive of optically thin gyrosynchrotron emission. If such emission traces magnetic reconnection events, then this could have consequences for both UCD magnetic models and the atmospheric stability of planets in orbit around them. Overall, our results provide confirmation that gyrosynchrotron radiation in radio-loud UCDs can remain detectable into the millimeter regime.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Global fire modelling and control attributions based on the ensemble machine learning and satellite observations
Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R 2 = 0.90, P < 0.001), total fire numbers (R 2 = 0.86, P < 0.001), and averaged fire size (R 2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (–1.9 Mha/yr, P < 0.01), followed by climate control (–1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.
54 ENVIRONMENTAL SCIENCES↗
Attribution of changes in global wetland methane emissions from pre-industrial to present using CLM4.5-BGC
Not Available
54 ENVIRONMENTAL SCIENCES↗
Dynamical Response of Mediterranean Precipitation to Greenhouse Gases and Aerosols
Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic Oscillation-Arctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901-2010, roughly one-third (31+/-17%) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol-cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and the economy in the Mediterranean region.
Tang, Tao↗
Global Surface Temperature Change and Uncertainties Since 1861
The objective of this talk is to analyze the warming trend and its uncertainties of the global and hemi-spheric surface temperatures. By the method of statistical optimal averaging scheme, the land surface air temperature and sea surface temperature observational data are used to compute the spatial average annual mean surface air temperature. The optimal averaging method is derived from the minimization of the mean square error between the true and estimated averages and uses the empirical orthogonal functions. The method can accurately estimate the errors of the spatial average due to observational gaps and random measurement errors. In addition, quantified are three independent uncertainty factors: urbanization, change of the in situ observational practices and sea surface temperature data corrections. Based on these uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 +/- 0.16 C between 1861 and 2000. This lecture will also touch the topics on the impact of global change on nature and environment. as well as the latest assessment methods for the attributions of global change.
Shen, Samuel S. P.↗
The Observed and Projected Changes of Global Monsoons: Current Status and Future Perspectives
The global monsoon system, encompassing the Asian-Australian, African, and American monsoons, sustains two-thirds of the world’s population by regulating water resources and agriculture. Monsoon anomalies pose severe risks, including floods and droughts. Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms. Observational data reveal a 20th-century shift: increased rainfall pre-1950s, followed by aridification and partial recovery post-1980s, driven by both internal variability (e.g., Atlantic Multidecadal Oscillation) and external forcings (greenhouse gases, aerosols), while ENSO drives interannual variability through ocean-atmosphere interactions. Future projections under greenhouse forcing suggest long-term monsoon intensification, though regional disparities and model uncertainties persist. Models indicate robust trends but struggle to quantify extremes, where thermodynamic effects (warming-induced moisture rise) uniformly boost heavy rainfall, while dynamical shifts (circulation changes) create spatial heterogeneity. Volcanic eruptions and proposed solar radiation modification (SRM) further complicate predictions: tropical eruptions suppress monsoons, whereas high-latitude events alter cross-equatorial flows, highlighting unresolved feedbacks. The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons. Critical challenges remain. Model biases and sparse 20th-century observational data hinder accurate attribution. The interplay between natural variability and anthropogenic forcings, along with nonlinear extreme precipitation risks under warming, demands deeper mechanistic insights. Additionally, SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation. Addressing these gaps necessitates enhanced observational networks, refined climate models, and interdisciplinary efforts to disentangle multiscale drivers, ultimately improving resilience strategies for monsoon-dependent regions.
climate extreme events↗
Global burned area increasingly explained by climate change
Fire behaviour is changing in many regions worldwide. However, nonlinear interactions between fire weather, fuel, land use, management and ignitions have impeded formal attribution of global burned area changes. Here, in this work, we demonstrate that climate change increasingly explains regional burned area patterns, using an ensemble of global fire models. The simulations show that climate change increased global burned area by 15.8% (95% confidence interval (CI) [13.1–18.7]) for 2003–2019 and increased the probability of experiencing months with above-average global burned area by 22% (95% CI [18–26]). In contrast, other human forcings contributed to lowering burned area by 19.1% (95% CI [21.9–15.8]) over the same period. Moreover, the contribution of climate change to burned area increased by 0.22% (95% CI [0.22–0.24]) per year globally, with the largest increase in central Australia. Our results highlight the importance of immediate, drastic and sustained GHG emission reductions along with landscape and fire management strategies to stabilize fire impacts on lives, livelihoods and ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Estimating Fallout Building Attributes from Architectural Features and Global Earthquake Model (GEM) Building Descriptions
A nuclear explosion has the potential to injure or kill tens to hundreds of thousands (or more) of people through exposure to fallout (external gamma) radiation. Existing buildings can protect their occupants (reducing fallout radiation exposures) by placing material and distance between fallout particles and individuals indoors. Prior efforts have determined an initial set of building attributes suitable to reasonably assess a given building’s protection against fallout radiation. The current work provides methods to determine the quantitative values for these attributes from (a) common architectural features and data and (b) buildings described using the Global Earthquake Model (GEM) taxonomy. These methods will be used to improve estimates of fallout protection for operational US Department of Defense (DoD) and US Department of Energy (DOE) consequence assessment models.
42 ENGINEERING↗
Global Ocean Mass Change Derived From Atmospheric Reanalyses
Barystatic sea level change is a significant climate variable and a gauge for water exchanges between land, atmosphere, and ocean, with consequential relevance to stakeholders. The monitoring and attribution of globally averaged sea level variations represents substantial challenges to the climate community. Atmospheric reanalyses provide a level of monitoring for elements of the climate system that are in communication with the ocean, including the atmosphere, terrestrial land, and the surfaces of glaciated land. Global atmospheric reanalyses comprise model output that is statistically adjusted to available observations. Potentially, reanalyses offer a climatic context to observed changes in water mass distribution. Improvement in the representation of the water cycle and in water mass conservation have been stated goals of reanalysis projects. Here, we assess the capability of atmospheric reanalyses to represent variations in the global water budget from the perspective of ocean mass balance. The purpose of this work is to provide a detailed evaluation of current atmospheric reanalyses and their ability to reproduce spatial and temporal variations in atmospheric and terrestrial water storage. A second goal is to identify and characterize recent apparent trends in the water balance. We examine the ERA5, MERRA-2, and JRA-3Q reanalyses with a focus on the radio-occultation observing period from 2007 to the present. For terrestrial water storage we also compare with ERA5-Land, a surface reanalysis. The primary validation is from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and its follow-on, GRACE-FO. The Atmospheric Infrared Sounder (AIRS) onboard NASA's Aqua satellite is also used for evaluation of the atmospheric moisture budget. Comparisons reveal strengths and weaknesses in the reanalyses of each component of the climate system. Reanalyses agree closely on atmospheric moisture storage trends, although mean values differ. Reanalyses show significant differences in land water storage. They also apply large corrections to the land water budget. Finally, reanalyses have differing abilities in representing the surface mass balance over glaciated land. Using the observed gravimetry record, changes in the underlying glaciated land ice are examined as a residual.
Richard Cullather↗
Global Total Ozone Recovery Trends Attributed to Ozone-Depleting Substance (ODS) Changes Derived From Five Merged Ozone Datasets
We report on updated trends using different merged zonal mean total ozone datasets from satellite and ground-based observations for the period from 1979 to 2020. This work is an update of the trends reported in Weber et al. (2018) using the same datasets up to 2016. Merged datasets used in this study include NASA MOD v8.7 and NOAA Cohesive Data (COH) v8.6, both based on data from the series of Solar Backscatter Ultraviolet (SBUV), SBUV-2, and Ozone Mapping and Profiler Suite (OMPS) satellite instruments (1978–present), as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone – Essential Climate Variable (GTO-ECV) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (both 1995–present), mainly comprising satellite data from GOME, SCIAMACHY, OMI, GOME-2A, GOME-2B, and TROPOMI. The fifth dataset consists of the annual mean zonal mean data from ground-based measurements collected at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. The addition of 4 more years consolidated the fact that total ozone is indeed slowly recovering in both hemispheres as a result of phasing out ozone-depleting substances (ODSs) as mandated by the Montreal Protocol. The near-global (60° S–60° N) ODS-related ozone trend of the median of all datasets after 1995 was 0.4 ± 0.2 (2σ) %/decade, which is roughly a third of the decreasing rate of 1.5 ± 0.6 %/decade from 1978 until 1995. The ratio of decline and increase is nearly identical to that of the EESC (equivalent effective stratospheric chlorine or stratospheric halogen) change rates before and after 1995, confirming the success of the Montreal Protocol. The observed total ozone time series are also in very good agreement with the median of 17 chemistry climate models from CCMI-1 (Chemistry-Climate Model Initiative Phase 1) with current ODS and GHG (greenhouse gas) scenarios (REF-C2 scenario). The positive ODS-related trends in the Northern Hemisphere (NH) after 1995 are only obtained with a sufficient number of terms in the MLR accounting properly for dynamical ozone changes (Brewer–Dobson circulation, Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). A standard MLR (limited to solar, Quasi-Biennial Oscillation (QBO), volcanic, and El Niño–Southern Oscillation (ENSO)) leads to zero trends, showing that the small positive ODS-related trends have been balanced by negative trend contributions from atmospheric dynamics, resulting in nearly constant total ozone levels since 2000.
Total Column Ozone Trends↗
Global Warming and 21st Century Drying
Global warming is expected to increase the frequency and intensity of droughts in the twenty-first century, but the relative contributions from changes in moisture supply (precipitation) versus evaporative demand (potential evapotranspiration; PET) have not been comprehensively assessed. Using output from a suite of general circulation model (GCM) simulations from phase 5 of the Coupled Model Intercomparison Project, projected twentyfirst century drying and wetting trends are investigated using two offline indices of surface moisture balance: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). PDSI and SPEI projections using precipitation and Penman- Monteith based PET changes from the GCMs generally agree, showing robust cross-model drying in western North America, Central America, the Mediterranean, southern Africa, and the Amazon and robust wetting occurring in the Northern Hemisphere high latitudes and east Africa (PDSI only). The SPEI is more sensitive to PET changes than the PDSI, especially in arid regions such as the Sahara and Middle East. Regional drying and wetting patterns largely mirror the spatially heterogeneous response of precipitation in the models, although drying in the PDSI and SPEI calculations extends beyond the regions of reduced precipitation. This expansion of drying areas is attributed to globally widespread increases in PET, caused by increases in surface net radiation and the vapor pressure deficit. Increased PET not only intensifies drying in areas where precipitation is already reduced, it also drives areas into drought that would otherwise experience little drying or even wetting from precipitation trends alone. This PET amplification effect is largest in the Northern Hemisphere mid-latitudes, and is especially pronounced in western North America, Europe, and southeast China. Compared to PDSI projections using precipitation changes only, the projections incorporating both precipitation and PET changes increase the percentage of global land area projected to experience at least moderate drying (PDSI standard deviation of < or = -1) by the end of the twenty-first century from 12 to 30 %. PET induced moderate drying is even more severe in the SPEI projections (SPEI standard deviation of > or = -1; 11 to 44 %), although this is likely less meaningful because much of the PET induced drying in the SPEI occurs in the aforementioned arid regions. Integrated accounting of both the supply and demand sides of the surface moisture balance is therefore critical for characterizing the full range of projected drought risks tied to increasing greenhouse gases and associated warming of the climate system.
moisture↗
Quantifying Aerosol Direct Effects from Broadband Irradiance and Spectral Aerosol Optical Depth Observations
We outline a methodology using broadband and spectral irradiances to quantify aerosol direct effects on the surface diffuse shortwave (SW) irradiance. Best Estimate Flux data span a 13 year timeframe at the Department of Energy Atmospheric Radiation Measurement Program’s Southern Great Plains (SGP) site. Screened clear-sky irradiances and aerosol optical depth (AOD), for solar zenith angles ≤ 65°, are used to estimate clear-sky diffuse irradiances. We validate against detected clear-sky observations from SGP’s Basic Radiation System (BRS). BRS diffuse irradiances were in accordance with estimates, producing a root-mean-square error and mean bias errors of 4.0 W/m2 and -1.4 W/m2, respectively. Absolute differences show 99% of estimates within ±10 W/m2 (10%) of the mean BRS observations. Clear-sky diffuse estimates are used to derive quantitative estimates of aerosol radiative effects, represented as the aerosol diffuse irradiance (ADI). ADI is the contribution of diffuse SW to global SW, attributable to scattering of atmospheric transmission by natural plus anthropogenic aerosols. Estimated slope for the ADI as a function of AOD indicates an increase of ~22 W/m2 in diffuse SW for every 0.1 increase in AOD. Such significant increases in the diffuse fraction could possibly increase photosynthesis. Annual mean ADI is 28.2 W/m2, and heavy aerosol loading at SGP provides up to a maximum increase of 120 W/m2 in diffuse SW over background conditions. With regard to seasonal variation, the mean diffuse forcings are 17.2, 33.3, 39.0, and 23.6 W/m2 for winter, spring, summer, and fall, respectively.
broadband↗
Estimating carrying capacity for juvenile salmon using quantile random forest models
Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.
See, Kevin E.↗
Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system
More extreme and prolonged floods and droughts, commonly attributed to global warming, are affecting the livelihood of major sectors of the world’s population in many basins worldwide. While these events could introduce devastating socioeconomic impacts, highly engineered systems are better prepared for modulating these extreme climatic variabilities. Herein, we provide methodologies to assess the effectiveness of reservoirs in managing extreme floods and droughts and modulating their impacts in data-scarce river basins. Our analysis of multiple satellite missions and global land surface models over the Tigris-Euphrates Watershed (TEW; 30 dams; storage capacity: 250 km 3 ), showed a prolonged (2007–2018) and intense drought (Average Annual Precipitation [AAP]: < 400 km 3 ) with no parallels in the past 100 years (AAP during 1920–2020: 538 km 3 ) followed by 1-in-100-year extensive precipitation event (726 km 3 ) and an impressive recovery (113 ± 11 km 3 ) in 2019 amounting to 50% of losses endured during drought years. Dam reservoirs captured water equivalent to 40% of those losses in that year. Additional studies are required to investigate whether similar highly engineered watersheds with multi-year, high storage capacity can potentially modulate the impact of projected global warming-related increases in the frequency and intensity of extreme rainfall and drought events in the twenty-first century.
54 ENVIRONMENTAL SCIENCES↗
Matrix approach to land carbon cycle modeling: A case study with the Community Land Model
The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO 2 fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Altogether, the matrix approach can make a broad range of future modeling activities more efficient and effective.
59 BASIC BIOLOGICAL SCIENCES↗