Floodplain Inundation and Salinization From a Recently Restored First‐Order Tidal Stream
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54 ENVIRONMENTAL SCIENCES↗
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Community Data Mining Approach for Surface Complexation Database Development
This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. Here the framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches
Focal Area(s): This white paper addresses how explainable machine learning (ML) algorithms can improve insights gained from complex data (Focal Area 3). We will also address how ML approaches related to sensor compression and low energy AI hardware can be used for efficient data acquisition (Focal Area 1). Science Challenge: We will focus on the coupled water cycle and carbon cycle processes in terrestrial ecosystems and terrestrial-aquatic interfaces. Thus, our approaches will be centered around several data-model integration challenges indicated in EESD strategic plans for two science focus areas: Terrestrial Ecosystem Science and Hydrobiogeochemistry. We can use AI to correct systematic model errors due to biases in either data or model structure/processes. Error patterns in model predictions usually vary by region, model, season etc. But there are systematic patterns in them. e.g., soil moisture tends to be overestimated in the arid western continental United States underestimated in wetter eastern USA; some land surface models tend to underestimate moisture in wet seasons and overestimate in dry seasons. Moreover, the consequences of extreme events (e.g., drought, extreme flooding, storm surges associated with tropical storms, hurricanes) on carbon cycle processes are not well represented in ecosystem and Earth system models. Redox-sensitive processes (e.g., rapid oxidation/reduction of iron and other redox-sensitive elements in soil microsites subjected to fluctuated hydrology) in terrestrial-aquatic interfaces further challenge model predictions of hot-spots (and hot-moments) due to poor understanding of underlying mechanisms. Thus, AI/ML approaches can be used to learn patterns in the data and model errors and use them to build model equations and correct process-based model errors.
54 ENVIRONMENTAL SCIENCES↗
Editorial: Hydrology, ecology, and nutrient biogeochemistry at the terrestrial-aquatic interface
Terrestrial-aquatic interfaces (TAI) play important roles in mediating the exchange of water and chemicals between land, surface and subsurface water systems, which is tightly coupled with biogeochemical transformations that influence water quality and ecosystem health (Harvey and Gooseff, 2015; Harvey et al., 2019). Understanding and predicting the interactions between the hydrologic, ecologic, and biogeochemical processes at those interfaces is crucial for the sustainable management of water resources and promotion of healthy ecosystems under different environmental stresses and disturbances, including climate change, human activities, and more frequent occurrence of extreme events. Both process-based and data-driven approaches have emerged to address critical challenges at TAIs as integral parts of the Earth system (e.g., Ward and Packman, 2019; Chen et al., 2021; Dwivedi et al., 2022). In this Research Topic, we sought research that advances the understanding of coupled hydrologic, ecologic, and biogeochemical processes along various TAIs from the summit to sea, e.g., river corridors and coastal TAI systems. We invited observational, experimental, theoretical, analytical, numerical, and data-driven research that aims to understand hydro-biogeochemical processes such as the redox dynamics and biogeochemical transformations of carbon, nutrients, and metals occurring at the TAIs and address their heterogeneity and scaling challenges.
54 ENVIRONMENTAL SCIENCES↗
Improved Mars Upper Atmosphere Climatology
The detailed characterization of the Mars upper atmosphere is important for future Mars aerobraking activities. Solar cycle, seasonal, and dust trends (climate) as well as planetary wave activity (weather) are crucial to quantify in order to improve our ability to reasonably depict the state of the Mars upper atmosphere over time. To date, our best information is found in the Mars Global Surveyor (MGS) Accelerometer (ACC) database collected during Phase 1 (Ls = 184 - 300; F10.7 = 70 - 90) and Phase 2 (Ls = 30 - 90; F10.7 = 90 - 150) of aerobraking. This database (100 - 170 km) consists of thermospheric densities, temperatures, and scale heights, providing our best constraints for exercising the coupled Mars General Circulation Model (MGCM) and the Mars Thermospheric General Circulation Model (MTGCM). The Planetary Data System (PDS) contains level 0 and 2 MGS Accelerometer data, corresponding to atmospheric densities along the orbit track. Level 3 products (densities, temperatures, and scale heights at constant altitudes) are also available in the PDS. These datasets provide the primary model constraints for the new MGCM-MTGCM simulations summarized in this report. Our strategy for improving the characterization of the Mars upper atmospheres using these models has been three-fold : (a) to conduct data-model comparisons using the latest MGS data covering limited climatic and weather conditions at Mars, (b) to upgrade the 15-micron cooling and near-IR heating rates in the MGCM and MTGCM codes for ad- dressing climatic variations (solar cycle and seasonal) important in linking the lower and upper atmospheres (including migrating tides), and (c) to exercise the detailed coupled MGCM and MTGCM codes to capture and diagnose the planetary wave (migrating plus non-migrating tidal) features throughout the Mars year. Products from this new suite of MGCM-MTGCM coupled simulations are being used to improve our predictions of the structure of the Mars upper atmosphere for the upcoming MRO aerobraking exercises in 2006. A Michigan website, containing MTGCM output fields from previous climate simulations, is being expanded to include new MGCM-MTGCM simulations addressing planetary wave influences upon thermospheric aerobraking fields (densities and temperatures). In addition, similar MTGCM output fields have been supplied to the MSFC MARSGRAM - 200X empirical model, which will be used in mission operations for conducting aerobraking maneuvers.
Bougher, S. W.↗
Connecting Paleo and Modern Oceanographic Data to Understand Atlantic Meridional Overturning Circulation Over Decades to Centuries
Modeling is an important tool for understanding AMOC on all timescales. Mechanistic studies of modern AMOC variability have been hampered by a lack of consistency between free-running models and the sensitivity of AMOC to resolution and parameterization. Recent work within the framework of the phase two Coordinated Ocean- Reference Experiments (CORE-II) addresses this issue head on, looking at model differences of AMOC mean state and interannual variability. One consistent feature across the models is that AMOC mean transport is related to mixed layer depths and Labrador Sea salt content, whereas interannual variability is primarily associated with Labrador Sea temperature anomalies. This is consistent with the hypothesized importance of salt balance for AMOC variability on geological timescales. The simulated relationships between AMOC and subsurface temperature anomalies in fully coupled climate models reveal subsurface AMOC fingerprints that could be used to reconstruct historical AMOC variations at low frequency.With the lack of long-term AMOC observations, models of ocean state that assimilate observational data have been explored as a way to reconstruct AMOC, but comparisons between models indicate they are quite variable in their AMOC representations. Karspeck et al. (2015) found that historical reconstructions of AMOC in such models are sensitive to the details of the data assimilation procedure. The ocean data assimilation community continues to address these issues through improved models and methods for estimating and representing error information.Two objectives of paleoclimate modeling are 1) to provide mechanistic information for interpretation of paleoclimate observations, and 2) to test the ability of predictive models to simulate Earth's climate under different background forcing states. In a good example of the first objective, Schmittner and Lund (2015) and Menviel et al. (2014) provided key information about the proxy signals expected under freshwater disturbance of AMOC, which were used to support the paleoclimate observations made by Henry et al. (2016). In an example of the second objective, Muglia and Schmittner (2015) analyzed Third Paleoclimate Modeling Intercomparison Project (PMIP3) models of the Last Glacial Maximum (LGM) and found consistently more intense and deeper AMOC transports relative to preindustrial simulations, counter to the paleoclimate consensus of LGM conditions, indicating that some processes are not well represented in the PMIP3 models. One challenge is to find adequate paleo observations against which to test these models. PMIP is now in phase 4 (part of CMIP6), which includes experiments covering five periods in Earth's history: the last millennium, last glacial maximum, last interglacial, and the mid-Pliocene. Newly compiled paleoclimate datasets from the PAGES2k project, more transient simulations, and participation of isotope enabled models planned for CMIP6PMIP4 will enable richer paleo data-model comparisons in the near future.
Kilbourne, Hali↗
Comment on “Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5-T2m” by N. Scafetta (2022)
Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that “all models with ECS > 3.0°C overestimate the observed global surface warming” is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion “that spatial t-statistics rejects the data-model agreement” is also not sustainable.
CMIP6↗