Land surface water is a key component of the global water cycle. Compared to remote sensing by satellites, both temporal extension and spatial continuity is superior in modeling of water surface area. However, overall evaluation of models representing different kinds of surface waters at the global scale is lacking. We estimated land surface water area (LSWA) using the Catchment-based Macro-scale Floodplain model (CaMa-Flood), a global hydrodynamic model, and compared the estimates to Landsat with 3″ spatial resolution at the global scale. Results show that the two methodologies show agreement in the general spatial patterns of LSWA (e.g., major rivers and lakes, open-to-sky floodplains), but globally consistent mismatches were found under several land surface conditions. CaMa-Flood underestimates LSWA in high northern latitudes (e.g., the Canadian Shield) and coastal areas, as the presence of isolated lakes in local depressions or small coastal rivers is not considered by the model’s physical assumptions. In contrast, model-estimated LSWA is larger than Landsat estimates in forest-covered areas (e.g., Amazon basin) due to the opacity of vegetation for optical satellite sensing, and in cropland areas due to the lack of dynamic water processes (e.g., re-infiltration, evaporation, water consumption) and constraints of water infrastructure (e.g., canals, levees). These globally consistent differences can be reasonably explained by the model’s physical assumptions or optical satellite sensing characteristics, and applying filters (e.g., floodplain topography mask, forest and cropland mask) to the two datasets allows the remaining local-scale discrepancies to be attributed to locally varying factors (e.g., channel parameters, atmospheric forcing).

The ability of current global models to simulate the transport of CO2 by mid-latitude, synoptic-scale weather systems (i.e. CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory-2 model intercomparison project (OCO-2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)-America mission. We quantify model-data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid-latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO-2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer-to-free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid-latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates.

The role of individual and collective human action is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems towards either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services towards some human end. We trace the salient features of modeled representations of human systems by organizing the typology around three key questions: 1) Who are the actors in MSD systems? 2) What are their actions? 3) How and for what purpose are these actors and actions operationalized in a computational model? We use this typology to critically examine existing models and chart the frontier of human systems modeling for MSD research.

Semidiurnal variability of alongshore currents on the inner shelf of the Southern California Bight is investigated using a seven year velocity and pressure time series. Analysis reveals that the M2-frequency alongshore current varies significantly over spatial scales of O(10km), inconsistent with the expected progressive surface tide. Instead, the observed variability is attributed to the influence of a northward-propagating, superinertial coastal trapped wave (CTW) that generates a quasi-barotropic (QBT) flow, defined as the portion of the depth-averaged alongshore current that is out of phase with the surface tide. A superinertial CTW model, forced by realistic bathymetry and stratification conditions, suggests that the dominant mode of variability is likely a mode-1 CTW with a wavelength of approximately 50km. The observations and model also reveal that seasonal changes in stratification modulate the wavelength and phase speed of the CTW, leading to a seasonal pattern in the phasing of the quasi-barotropic alongshore flow. These findings provide a new perspective on the complex dynamics governing semidiurnal variability of alongshore currents on the inner shelf of the Southern California Bight and highlight the importance of considering the effects of superinertial CTWs when examining coastal dynamics.

Natural climate solutions (NCS) have the potential to achieve up to one-third of emission reductions, but uncertainties surrounding their effectiveness hinder their full realization. Here we employ modern portfolio theory to build NCS portfolios (NCSPs) including a variety of pathways listed in Griscom et al. 1. The different pathways are treated as risky assets within a 'carbon mitigation market' with their returns and risks defined by global estimates of mitigation potential. Our aim is to maximize carbon sequestration while minimizing the risk of carbon loss, thus effectively navigating the 'efficient frontier', where the best trade-off between maximum carbon sequestration and risk occurs. Diversifying pathways leads to decreased risk and enhanced resilience, particularly when risks of carbon loss due to environmental stressors are spatially or temporally uncorrelated. The optimal NCSPs provide valuable insights into distributing investments and land within pathway categories (forests, agriculture and wetlands), intervention types (e.g., manage, protect, restore), cost-effectiveness, and geographical contexts. We hope these results help inform policymakers to reduce risk while pursuing ambitious carbon mitigation targets.

Isolating the source of non-tidal oceanographic noise in seafloor pressure data is critical for improving the use of these data for seafloor geodetic applications. Residuals between nearby bottom pressure records have typically been used to remove the non-tidal components, as these are largely common-mode. To evaluate the similarities between pairs of observed bottom pressure records at a range of water depths, we calculate the standard deviations of the time series of residuals between data from all site pairs, recorded during a recent experiment offshore New Zealand. Similar to a recent study offshore Cascadia, we find that the magnitude of the standard deviation depends more on relative water depth than the distance between sites. This confirms that non-tidal components are more similar along isobaths even if the distance between sites is large. We show that the depth range varies with the depth of the deeper site of the pairs under restrictions.

The role of groundwater in maintaining streamflow in the alpine area with distribution of permafrost and seasonal frost is a poorly studied topic of considerable interest. The stream and groundwater interactions and groundwater contributions to the Heihe River were investigated during this study in a representative subcatchment in the headwater region of the Heihe River Basin, the northeastern Qinghai-Tibet Plateau of China. The hydraulic, chemical and isotopic data as well as Bayesian mixing model results show that groundwater-stream water interactions were both spatially and temporally variable. The tributaries were primarily recharged by springs within the permafrost zone during the frozen period when the water source and sediments were frozen and the groundwater discharged to the mainstream within the seasonal frost zone to maintain the streamflow. The groundwater contribution to mainstream discharge decreased from 95% during the frozen period to 80-90% in the thawing period due to the inflow from tributaries. However, the stream and groundwater interactions vary several times along altitude during the thawed period from June to early September, due to the increased glacial/snow meltwater volume, deepened active layer, and melted seasonal frost. Groundwater contribution decreased to ~40-60% of the mainstream discharge during the thawed period because tributary streams contribution largely increased. As shown by ~70-90% contribution from groundwater to the mainstream discharge, the mainstream flow mainly sourced from the release of groundwater in aquifers in the freeze-back period. These data indicate that the variations in groundwater-surface water interactions were largely influenced by the distribution and freeze-thaw cycle of permafrost and seasonal frost. The importance of groundwater storage in maintaining streamflow in the Heihe headwater region was highlighted by this study.

A document by Triparna Sett. Click on the document to view its contents.

The effect of polar sea ice melt on low latitude climate is little known. In order to understand the response of Indian summer monsoon (ISM) to the sea ice melt, we have run a suite of coupled and uncoupled climate model simulations. In one set of simulations, the albedo of sea ice is changed in such a way that it would melt as a result of increased absorption of solar radiation. We find a substantial weakening of ISM in sea ice melt experiments. Further, the genesis frequency of monsoon low-pressure systems (LPS) declines by about 40% in the sea ice melt simulations. A weakening and equatorward shift of ITCZ causes the decline in LPS genesis. Overall, the response of ISM to the sea ice melt resembles the response to greenhouse gas induced warming.

c\cite{Beck_2021,Claudet_2020,Cunsolo_2018,Franke_2020,process,Jung_2022,Kemmis_2014} In 2021, and again in 2022, our globally scattered group of artists and scientists followed the Exquisite Corpse process for co-creating multiple threaded series of artworks inspired by extreme ocean events. In 2023 some members of our two groups convened a series of collaborative synchronous sessions to create a shared understanding of the paths we took to join this creative community and engage in this open ArtScience collaboration.Prompted by a sense of curiosity, a shared passion for the ocean and the will to collaborate, artists and scientists came together (virtually) to forge relationships around the shared intent to deepen our understanding of transdisciplinary ArtScience and how it can contribute to ocean knowledge. Discovering common ground (and creating a grounded commons) and mutual interests, we set forth on a path of curiosity into a formal Exquisite Corpse cycle. We created an array of art projects drawn from a semi-ambiguous “seed” within a semi-safe, not-always-comfortable space for experimentation and creation. Through cyclical introspection, enactment, opening, giving, and receiving, we developed our artwork series and relationships among our group. Finally, in the sharing of these series, we experienced new insights about our individual and collective perceptions of extreme ocean events, while simultaneously further deepening our relationships and appreciation for the potency of ArtScience collaborations.This poster was presented at the AGU23 Meeting in San Francisco, CA, on 13 December 2023. Conference abstract URL: https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1266010

Regional numerical modeling provides a valuable means to assess important aspects of natural systems when the cost and effort of direct observation is impractical; the Arctic Ocean and its marginal seas and archipelagos represent a domain where such applications abound. We describe the configuration of and results from a set of coupled sea-ice ocean circulation models that are based on the Regional Ocean Modeling System (ROMS). Challenging features include large fresh water fluxes from the major Arctic rivers, seasonal land-fast ice, and ice-covered open boundary conditions in nested models. A broad-scale domain, dubbed the Pan-Arctic ROMS (PAROMS) model, extends from the Aleutian Islands to southern Greenland using a telescoping horizontal grid spacing that varies from 4 km in the Pacific to 8 km in the Atlantic. Higher-resolution domains include nested grids at 3 km and 500 m grid spacing. Coastal discharges are prescribed as lateral inflows distributed over the depth of the ocean-land interface. The model includes tides, sea ice, updated bathymetry, and atmospheric forcing from the MERRA reanalysis. We assess the model’s performance with respect to tides, storm surges, wind-driven circulation, and thermohaline fields. A hindcast integrated over 1983-2015 provides a means to assess synoptic, seasonal and inter-annual variability. Applications include investigations of shelf flow field pathways, residence times and advective timescales, and energetics balances.

Within Arctic deltas, surficial hydrologic connectivity of lakes to nearby river channels influences physical processes like sediment transport and ice phenology as well as biogeochemical processes such as photochemistry. As the Arctic hydrologic cycle is impacted by climate change, it is important to quantify temporal variability in connectivity. However, current connectivity detection methods are either spatially limited due to data availability constraints or have been applied at only a single time step. Additionally, the relationship between connectivity and lake ice is still poorly quantified. In this study, we present a multitemporal classification and validation of lake connectivity in the Colville River Delta, AK. We introduce a connectivity detection algorithm based on remote sensing of water color that is expandable to other high-sediment Arctic deltas. Comparison to validation datasets suggests that detection of high vs. low connectivity lakes is accurate in 69.5–85.5% of cases. Connectivity temporally varies in about 20% of studied lakes and correlates strongly with discharge and lake elevation, supporting the idea that future changes in discharge will be drivers of future changes in connectivity. Lakes that are always highly connected start and end ice break up an average of 26 and 16 days earlier, respectively, compared to lakes that are never connected. Because spring and summer ice conditions drive Arctic lake photochemistry processes, our research suggests that surface connectivity is an important parameter to consider when studying biogeochemistry of Arctic delta lakes.

Water availability in the dry Western United States (US) under a warming climate and increasing water use demand has become a serious concern. Previous studies have projected future runoff changes across the Western US but ignored the impacts of ecosystem response to elevated CO2 concentration. Here, we aim to understand the impacts of elevated CO2 on future runoff changes through ecosystem responses to both rising CO2 and associated warming using the Noah-MP model with representations of vegetation dynamics and plant hydraulics. We first validated Noah-MP against observed runoff, LAI, and terrestrial water storage anomaly from 1980–2015. We then projected future runoff with Noah-MP under downscaled climates from three climate models under RCP8.5. The projected runoff declines variably from the Pacific Northwest by –11% to the Lower Colorado River basin by –92% from 2016–2099. To discern the exact causes, we conducted an attribution analysis of two additional sensitivity experiments: one with constant CO2 and another with monthly LAI climatology based on the Penman-Monteith equation. Results show that surface “greening” (due to the CO2 fertilization effect) and the stomatal closure effect are the second largest contributors to future runoff change, following the warming effect. These two counteracting CO2 effects are roughly compensatory, leaving the warming effect to remain the dominant contributor to the projected runoff declines at large river basin scales. This study suggests that both surface “greening” and stomatal closure effects are important factors and should be considered together in water resource projections.

A document by Dan Sandiford. Click on the document to view its contents.

The lunar surface consists of a regolith layer, i.e., fragmental and unconsolidated rock material of highly varied character, which covers the underlying bedrock. Understanding the structure and composition of the lunar regolith and the underlying bedrock is very important to reveal geologic features of the Moon, explore natural resources humans can exploit, and obtain valuable information regarding the history of the solar system. Electromagnetic radiations from the surface of the Moon at microwave frequencies depends on the physical, chemical, and electrical properties of the regolith layer and the bedrock. Moreover, the electromagnetic penetration depth changes with frequency which allows wideband microwave radiometers operating below 50 GHz to conveniently profile these properties versus depth to characterize the lunar regolith and bedrock with wide spatial coverage. Combining current models of depth-dependent physical and chemical properties with simple electromagnetic forward radiation models, surface emissions over the Apollo sites were simulated and compared with Chinese Chang’E-1 and Chang’E-2 multi-frequency microwave radiometer measurements. Potential sources of the differences between the simulations and measurements have been identified as the temperature dependence of the electrical properties of the regolith, bedrock type, surface and bedrock roughness, as well as density fluctuations, scattering due to inhomogeneities, and coherent interference of electromagnetic fields within the regolith. However, many of these subjects have been studied thoroughly for the remote sensing of the cryosphere; thus leveraging this experience with the rapid developments in radiometer designs, wideband microwave radiometers promise valuable scientific returns in future lunar missions.

The Slow-Slip hypothesis is postulated on two observations– existence of tectonic tremors and their spatio-temporal correlation with anomalous slow reversals in horizontal geodetic measurements. The above observations have led geoscientists to believe that the down-dip portion of the plate interface is slowly shearing and releases energy gradually in the form of tremor. However, numerous observations and scientific findings are poorly explained by the Slow-Slip hypothesis. Here, we show that periodic seismic activity and geodetic changes, result from the episodic buckling of the overriding continental crust and its rapid collapse on the subducting oceanic slab. According to the Episodic Buckling and Collapse hypothesis, geodetic measurements, previously inferred as slow slip, are the surficial expressions of slowly-evolving buckling and rapid collapse of the over-riding plate, while tremor swarms result from the striking of the collapsing overriding plate on the subducting slab (as opposed to slipping or shearing).

Convection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in 3D models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of parameterizations to various perturbations rather than their behavior under particular strong forcing. We use the linear response function method proposed by Kuang (2010) to compare twelve physical packages in five atmospheric models using single-column model (SCM) simulations under idealized radiative-convective equilibrium conditions. The models are forced with anomalous temperature and moisture tendencies. The temperature and moisture departures from equilibrium are compared with published results from a cloud-resolving model (CRM). Results show that the procedure is capable of isolating the behavior of a convection scheme from other physics schemes. We identify areas of agreement but also substantial differences between convection schemes, some of which can be related to scheme design. Some aspects of the model linear responses are related to their RCE profiles (the relative humidity profile in particular), while others constitute independent diagnostics. All the SCMs show irregularities or discontinuities in behavior that are likely related to switches or thresholds built into the convection schemes, and which do not appear in the CRM. Our results highlight potential flaws in convection schemes and suggest possible new directions to explore for parameterization evaluation.

Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geo-physical properties of a catchment, might improve the calibration procedure, increase process realism and can enable prediction in ungauged areas. We present the Function Space Optimization (FSO), a symbolic regression method for estimating parameter transfer functions for distributed hydrological models. FSO is based on the idea of transferring the search for mathematical expressions into a continuous vector space that can be used for optimization. This is accomplished by using a text generating neural network with a variational autoencoder architecture, that can learn to compress the information of mathematical functions. To evaluate the performance of FSO, we conducted a case study using a parsimonious hydrological model and synthetic discharge data. The case study consisted of two FSO applications: Single-criteria FSO, where only discharge was used for optimization and multi-criteria FSO, where additional spatiotemporal observations of model states were used for transfer function estimation. The results show that FSO is able to estimate transfer functions correctly or approximate them sufficiently. We observed a reduced fit of the parameter density functions resulting from the inferred transfer functions for less sensitive model parameters. For those it was sufficient to estimate functions resulting in parameter distributions with approximately the same mean parameter values as the real transfer functions. The results of the multi-criteria FSO showed that using multiple spatiotemporal observations for optimization increased the quality of estimation considerably.