A document by Roy Barkan. Click on the document to view its contents.

Current predictions for the changing Arctic focus on the evolution of sea ice from perennial to seasonal cover, with sea ice thinning as the Arctic warms. Significant efforts in developing climate forecasts for the Arctic on the decadal time-frame are underway. But, ships in transit inside the Arctic basin require high-resolution, near-term forecasts to provide guidance on scales of interest to their operations: 1-5 km. Climate predictions are insufficient for such short-term forecasts, as they are computationally intensive and at lower resolution than needed for single vessel operations. Deep learning techniques can offer the capability to enable rapid assimilation and analysis of multiple sources of information for these critical sea ice forecasts. In this study, data from NOAA’s Global Forecast System (GFS), Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature (SST), and Multisensor Analyzed Sea Ice Extent (MASIE) data from the National Snow and Ice Data Center (NSIDC) were used to develop the sea ice extent forecast model. All layers were sampled to the same 1 km Equal-Area Scalable Earth (EASE) grid. Sea ice prediction was achieved for subsequent weeks from seven days prior plus seven days of historical forecast data. The novelty in this approach was using forecast data as data to aid in the prediction of sea ice extent. The models were trained using data from the freeze-up periods of 2016, 2018, 2019, and 2020. A four-fold cross-validation was performed, with each year held out for validation. Our deep learning models are trained using overlapping 256x256 pixel tiles over the entire region of interest, the Beaufort Sea. Sigmoid outputs are averaged together in overlap regions, weighted by the distance from the edge to ensure smooth transitions from one subregion to the next. These are converted to a binary ice mask by thresholding at 0.5, then computing a per-pixel accuracy, which is averaged over the ocean/ice regions. Accuracy for a baseline persistence model was also computed over the seven-day forecast period. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days one to seven, the deep learning model accuracy dropped only to 93%. With these initial positive results, we plan to extend the model outputs from ice extent to ice concentration and thickness in future work.

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.

Recent climate change is characterised by rapid global warming, but the goal of the Paris Agreement is to achieve a stable climate where global temperatures remain well below 2°C above pre-industrial levels. Inferences about conditions at or below 2°C are usually made based on transient climate projections. To better understand climate change impacts on natural and human systems under the Paris Agreement, we must understand how a stable climate may differ from transient conditions at the same warming level. Here we examine differences between transient and quasi-equilibrium climates based on greenhouse gas-only model simulations at 1.5°C and 2°C global warming. We find substantial local differences between seasonal-average temperatures, with mid-latitude land regions in boreal summer considerably warmer in a transient climate than a quasi-equilibrium state at both 1.5°C and 2°C global warming. Our research demonstrates that the rate of global warming must be considered in regional projections.

The need to better understand climate change has driven model simulations to greater fidelity with improved spatiotemporal resolution (e.g., < 10 km at sub-hourly cadence). For example, the 7 km GEOS-5 Nature Run (G5NR) with 30-minute outputs from 2005-07 at the NASA Center for Climate Simulation (NCCS) is ~4 PB and is not easily portable. The rise of these high-fidelity climate models coincides with the emergence of cloud computing as a viable platform for scientific analytics. NASA has adopted a cloud computing strategy using public providers like Amazon Web Services (AWS). However, it is not cost- or time- effective to move the High- Performance Computing (HPC)-based model computations and data to the cloud. Thus, there is a need for scalable model evaluation compatible with both the cloud and HPC platforms like NCCS. To fill this need we have extended the analytics component of the Apache Science Data Analytics Platform (SDAP) with a streamlined version that specifically targets high-resolution science data products and climate model outputs on a regular coordinate grid. Gridded inputs (as opposed to other data structures like point clouds or swath-based measurements supported by SDAP), enable offsets to particular grid cells to be directly computed, allow for processing on the original NetCDF or HDF granules, do not require a second tiled copy of the data, and accommodate a simpler technology stack since no geospatial database is required for lookups or tile storage. Our core module, Parmap, abstracts the map-reduce model so that users can select from a variety of map computational modes, including Spark, Dask, serverless AWS Lambda, PySparkling, and Python multiprocessing. Example analytics include area-averaged time series and time-averaged, correlation and climatological maps. Benchmarks compare favorably with the full SDAP implementation.

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

We analyze a large database of abyssal peridotite clinopyroxene compositions using principal component analysis (PCA) and k-means clustering, to better understand clinopyroxene compositional systematics in abyssal peridotites. We combine this analysis with open-system melting models to investigate the potential sources of compositional variation. PCA shows that 84% of the variation in clinopyroxene compositions can be represented using only 2-dimensional information (PC1 and PC2 values). We use k-means clustering to classify clinopyroxene compositions into four clusters. Clusters 1–3, representing 85% of the data, show progressive depletions in LREE/HREE, and are associated with decreases in Na2O in clinopyroxene, and general increases in Cr# of spinel. We interpret peridotites with clinopyroxene compositions from clusters 1–3 to represent residues of partial melt extraction. The degree of melt extraction increases from cluster 1 to 3, and exerts a primary control on compositional variations. The presence or absence of garnet-field melting prior to spinel-field melting and the retained melt fraction during partial melting exert secondary controls on clinopyroxene compositions. Cluster 4 clinopyroxenes, representing 15% of data, show less fractionated LREE/HREE with low-HREE abundances, elevated Sr, and depleted signatures in their host peridotites. Clinopyroxene compositions in cluster 4 are only reconstructed in our models where melt-rock interaction follows partial melting, suggesting that peridotites with cluster 4 clinopyroxenes have experienced both of these processes. Clusters 1–4 are observed in most ridges, reflecting compositional heterogeneity on each ridge. This variability reflects variations in the degree of partial melting, amount of garnet-field melting, retained melt fractions, and melt-rock interaction.

The transition from the Semail ophiolite mantle to the underlying metamorphic sole was drilled at ICDP OmanDP Hole BT1B. We analyzed the bulk major, volatile and trace element compositions of the mantle-derived listvenite series and metamorphic rocks, with the aim to constrain chemical transfers associated to peridotite carbonation along the ophiolite basal thrust. The listvenite series comprise variously carbonated serpentinites and (fuchsite-bearing) listvenites. They have high CO2 (up to 43.2 wt.%) and variable H2O (0-12.1 wt.%). Yet, they have compositions close to that of the basal banded peridotites for most major and lithophile trace elements, with fuchsite-bearing listvenites overlapping in composition with amphibole-bearing basal lherzolites (e.g., Al2O3= 0.1-2.2 wt.%; Yb= 0.05-1 x CI-chondrite), The protolith of the listvenite series was likely similar in structure and composition to serpentinized banded peridotites which immediately overlie the metamorphic sole elsewhere in Oman. The listvenite series are enriched in fluid mobile elements (FME) compared to Semail peridotites (up to ~103-104 x Primitive Mantle), with concentrations similar to the underthrusted metabasalts and/or metasediments for Cs, Sr and Ca and sometimes even higher for Pb, Li, As, and Sb (e.g., Li up to 130 ppm; As up to 170 ppm). We also observe a decoupling between Sr-Ca enrichments and other FME, indicating interactions with several batches of deep CO2-rich fluids transported along the basal thrust. These results suggest that peridotite carbonation could represent one of the major trap-and-release mechanisms for carbon, water and FME along convergent margins.

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.

Marine Isotope Stage (MIS) 5c, between ~106,000 and ~93,000 years ago, represents an important warm period on Earth in which the current anthropogenic warming can be contextualized. Although viewed as a pronounced interstadial, its climate expression is regionally disparate, with different regions on Earth showing evidence of either cooler conditions than modern-day or warmer conditions than modern-day. It is therefore important to expand temperature reconstructions to different regions on Earth to gain a better picture of climate dynamics during MIS 5c. In Alaska, there are no quantitative temperature reconstructions for MIS 5c, vastly limiting our knowledge of temperature changes in this climatically sensitive high-latitude region. Here, we fill-in this gap by providing the first quantitative temperatures from MIS 5c in Alaska using hydrogen isotopes from fluid inclusions in precisely dated speleothems. We find that regional temperatures during MIS 5c were within error of modern-day (2021 CE) temperatures, likely representing the most recent time period that regional temperatures were as high as modern-day.

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.

The past 2 decades of research has demonstrated the value of co-production as a process that creates information that is usable to decision makers. However, research has also shown that those organizations that are already larger, more technically-capable, and located closer to the sources of new information are more likely to engage in co-production with boundary organizations. The question then arises of how smaller entities, who may actually have greater need for capacity, can benefit from investments in new science aimed at improving decision making for water management. In this project we conducted 5 case studies of the information use preferences and practices among small-scale water systems (small municipalities and Water Conservancy Districts) in the Upper Colorado River Basin to understand the opportunities and constraints for the uptake of new sources of information. Like previous work, results indicated that scale, skill, understandability, and lack of capacity limited the use of available information. Furthermore, entities did not engage in co-production with knowledge-related boundary organizations to any extent. However, small water providers did consistently mention the value of contact with other water systems in the area, and the value of being able to reach out to certain key individuals who were looked to as trusted sources of opinion on current information products and trends. Managers emphasized the importance of experience in operating a water system and involvement in operations as critical factors that engendered trust in these key individuals. Finally, certain water systems were seen as leaders in the local area, and constantly mentioned as sources of information and innovative ideas. These findings suggest the importance of both key individual practitioners in pioneering and disseminating new information, as well as more broadly the role of a community of practice in reaching small water providers. Implications for boundary organizations and emerging networks are discussed.

This study examines the role of the mean state in the propagation of the Madden-Julian Oscillation (MJO) over the Maritime Continent (MC). We use an ensemble of simulations made with a single model - the Community Earth System Model version 2 (CESM2) – to assess the effect of the mean state that is unaffected by that of model components such as parameterization schemes. Results show that one ensemble member with an exceptionally stronger MJO propagation also exhibits a much steeper background meridional moisture gradient (MMG) over the southern MC region than the other ensemble members. The simulated mean state affects MJO via its impacts on moisture dynamics - a column water vapor budget reveals a greater advection of mean moisture by MJO wind in the southern MC is responsible for the anomalous MJO activity.

Geodetic fault slip inversions have been generally performed by employing a least squares method with a spatial smoothing constraint. However, this conventional method has various problems: difficulty in strictly estimating non-negative solutions, assumption that unknowns follow the Gaussian distributions, unsuitability for expressing spatially non-uniform slip distributions, and high calculation cost for optimizing many hyper-parameters. Here, we have developed a trans-dimensional geodetic slip inversion method using the reversible-jump Markov chain Monte Carlo (rj-MCMC) technique to overcome the problems. Because sub-fault locations were parameterized by the Voronoi partition and were optimized in our approach, we can estimate a slip distribution without the spatial smoothing constraint. Moreover, we introduced scaling factors for observational errors. We applied the method to the synthetic data and the actual geodetic observational data associated with the 2011 Tohoku-oki earthquake and found that the method successfully reproduced the target slip distributions including a spatially non-uniform slip distribution. The method provided posterior probability distributions with the unknowns, which can express a non-Gaussian distribution such as large slip with low probability. The estimated scaling factors properly adjusted the initial observational errors and provided a reasonable slip distribution. Additionally, we found that checkerboard resolution tests were useful to consider sensitivity of the observational data for performing the rj-MCMC method. It is concluded that the developed method is a powerful technique to solve the problems of the conventional inversion method and to flexibly express fault-slip distributions considering the complicated uncertainties.

Two distinct rainfall stages over the Central Highlands (CH) of Vietnam during the rainy season have been objectively defined using the high-resolution Vietnam Gridded Precipitation dataset for 1983–2010 (28 years): a second rainy season (SRS) embedded in the conventional rainy season. Surprisingly, the pronounced interannual variation in the SRS onset date has led to three apparent regimes: an early (late) SRS with a 1 month longer (shorter) rainfall period occurring in early July (until mid-August) and a normal SRS starting in late July. Almost all the early SRS years occur during El Niño developing phases, particularly during the Niño3.4 sea surface temperature (SST) increase from January through December. Water vapor budget analyses reveal that the interannual variation in the divergent water vapor flux is in response to the warmer July tropical Pacific SST anomalies, resulting in rainfall enhancement over the CH and eventually inducing early SRS onset.

A new, simple parameterisation scheme for scalar (heat and moisture) exchange over sea ice and the marginal ice zone is tested in a numerical weather and climate prediction model. This new “Blended A87” scheme accounts for the influence of aerodynamic roughness on the relationship between momentum and scalar exchange over consolidated sea ice, in line with long-standing theory and recent field observations, and in contrast to the crude schemes currently operational in most models. Using aircraft observations and Met Office Unified Model simulations of cold-air outbreak (CAO) conditions over aerodynamically rough sea ice, we demonstrate striking improvements in model performance when the Blended A87 scheme replaces the model’s operational treatment for surface scalar exchange, provided that the aerodynamic roughness over consolidated ice is appropriately prescribed. The mean biases in surface sensible heat flux, surface latent heat flux, near-surface air temperature and surface temperature reduce from 25 to 11 W m-2, 22 to 12 W m-2, 0.8 to 0.0 K, and 1.4 to 0.8 K, respectively. We demonstrate that such impacts on surface exchange over sea ice can have a marked impact on the evolution of the atmospheric boundary layer across hundreds of kilometres downwind of the sea ice during CAO conditions in the model. Our results highlight the importance of spatiotemporal variability in the topography of consolidated sea ice for both momentum and scalar exchange over sea ice; accounting for which remains a challenge for modelling polar weather and climate.

The Atlantic’s Meridional Overturning Circulation (AMOC) is weak when the northern summer insolation is strong and vice-versa in the 23-kyr precessional frequency band. The mechanism behind this response is unknown and does not conform in any obvious way to Milankovitch theory. The link between the AMOC and precession is attributed here to the trade winds that blow out from Africa across the Atlantic. The trade winds, like the AMOC itself, are weak when the northern summer insolation is strong. The trade winds are shown here to have a direct impact on the AMOC’s return flow. A return flow altered this way should warm and cool the Earth in a way that has not been previously considered.

Magma emplacement in the top unconsolidated sediments of rift basins is poorly constrained in terms of mechanics and associated hydrothermal activity. Our study compares two shallow sills from the Guaymas Basin (Gulf of California) using core data and analyses from IODP Expedition 385, and high-resolution 2D seismic data. We show that magma stalling in the top uncemented sediment layer is controlled by the transition from siliceous claystone to uncemented silica-rich sediment, promoting flat sill formation. Space is created through a combination of viscous indentation, magma-sediment mingling and fluidization processes. In low magma input regions, sills form above the opal-A/CT diagenetic barrier, while high magma input leads to upward stacking of sills, forming funnel-shaped intrusions near the seafloor. Our petrophysical, petrographic, and textural analyses show that magma-sediment mingling creates significant porosity (up to 20%) through thermal cracking of the assimilated sediment. Stable isotope data of carbonate precipitates indicate formation temperatures of 70−90°C, consistent with the current background geothermal gradient at 250−325 m depth. The unconsolidated, water-rich host sediments produce little thermogenic gas through contact metamorphism, but deep diagenetically formed gas bypasses the low-permeability top sediments via hydrothermal fluids flowing through the magma plumbing system. This hydrothermal system provides a steady supply of hydrocarbons at temperatures amendable for microbial life, acting as a major microbial incubator. Similar hydrothermal systems are expected to be abundant in magma-rich young rift basins and play a key role in sustaining subseafloor ecosystems.