Understanding the simple mechanism of variable solar irradiance ocean warming/cooling allows for effective attribution, detection, and prediction of ocean-related extreme weather events based on solar activity level and duration. Modern and historical solar and ocean data were used to find the ocean warms from rising and high solar activity over any duration, and vice-versa, such as cooling from low activity during the Dalton Minimum. Equatorial ocean heat content and sea surface temperature are sensitive to daily total solar irradiance (TSI) variation and level, and to the upwelled heat accumulation from prior days sub-surface absorbed solar energy penetration. Equatorial evaporation from the absorbed solar energy performs as the pump of Earth’s hydrological system, sourcing atmospheric rivers and associated extreme weather events, follows a solar cycle pattern. Decadal scale ocean warming and post-solar cycle maximum El Nino events were empirically found to occur after solar activity rises above a decadal average of 120 sfu F10.7cm, equivalent to 94 v2 SIDC sunspot number and 1361.25 W/m2 LASP SORCE TSI. HadSST3 was found to linearly vary with the annual change in TSI, nominally at 0.5°C/W/year, varying with rate of TSI change. An empirical F10.7-TSI-SST model was derived combining a F10.7cm-SORCE TSI correlation model and the HadSST3-TSI sensitivity factor, predicated on the SWPC Solar Cycle 24 panel 2016 F10.7cm flux forecast. The author used this model in December of 2015 to uniquely and successfully predict the 2016 HadSST3 temperature fall to within 0.03°C. Cross correlation analysis indicate solar minimum La Nina events result from insufficient TSI over time, producing less equatorial evaporation, less cloud cover and precipitation, causing drought in the US. The Solar Modern Maximum that peaked in late 2003 warmed the ocean creating 20th century climate change via higher than average solar activity, higher than the 1361.25 W/m2 decadal TSI warming threshold. The 2003 European heat wave was forced by cumulative high solar activity warming of the AMO, and from high TSI during the heat wave. The 2013/14 Cold Wave is partly attributable to periods of lower than solar minimum level irradiance from October 2013 through February 2014. Future extremes depend on the duration of future solar cycle activity extremes.

Recent advances in Earth observation data and computing ability create exciting opportunities for national and global studies of human impacts to water resources. But, with a lack of complete databases of artificial levees, there remains a need to better understand how artificial levees impact floodplain extent at regional and larger scales. Here, we estimate river-floodplain disconnection in the contiguous United States using an incomplete artificial levee database, machine learning algorithms, and hydrogeomorphic floodplain delineation models. We tested different topographic, land use, and spatial variables with different machine learning techniques in a case study of seven geographically diverse HUC8 basins before applying the technique at the national scale. We found that a parsimonious random forest model without topographic variables was 97% accurate. When applied to areas within a national 100-year hydrogeomorphic floodplain, the model indicated the potential for more than 180,000 km of undocumented artificial levees, meaning that the National Levee Database (NLD) is about 20% complete. More than 62% of potential levees are concentrated in the Upper and Lower Mississippi and Missouri basins. The stream order distribution of potential and NLD levees are similar; however, potential levees are primarily located along stream orders 3 and 6 while the NLD locations are along stream orders 2, 3 and 4. Using this, we explored the national impacts of artificial levees on floodplain extent by comparing two hydrogeomorphic floodplains based on (1) an unmodified USGS 1 arc second DEM and (2) a modified DEM with known and potential levees erased from the topography. We found that the overall impact of artificial levee removal was to shift the location of flooding. Over 30% of the CONUS 100-year floodplain was cultivated or developed land use.

Earthquakes can be dynamically triggered by the passing waves of events from disconnected faults. The frequent occurrence of dynamic triggering offers tangible hope in revealing earthquake nucleation processes. However, the physical mechanisms behind earthquake dynamic triggering have remained unclear, and contributions of competing hypotheses are challenging to isolate with individual case studies. Therefore, developing a systematic understanding of the spatiotemporal patterns of dynamic triggering can provide insights into the physical mechanisms, which may aid mitigation of earthquake hazards. Here we investigate earthquake dynamic triggering in Southern California from 2008 to 2017 using the Quake Template Matching catalog and an approach free from assuming an earthquake occurrence distribution. We develop a new set of statistics to examine the significance of seismicity-rate changes as well as moment-release changes. We show that up to 70% of global M≥6 events may have triggered earthquakes in southern California and that the triggered seismicity often occurred several hours after the passing seismic waves. On average, earthquakes are triggered about every 4 days in the region, albeit at different locations. Although adjacent fault segments can be triggered by the same earthquakes, the majority of triggered earthquakes seem to be uncorrelated, suggesting that the process is primarily governed by local conditions. Further, the occurrence of dynamic triggering does not seem to correlate with ground motion (e.g., peak ground velocity) at the triggered sites. These observations indicate that nonlinear processes may have primarily regulated the dynamic triggering cases.

This work presents Bayesian network-informed conditional random forests (BNICRF), a novel multiresponse classification technique able to downscale the joint probability distribution of precipitation occurrence at a set of geographical locations of interest from large-scale predictors. BNICRFs combine a Bayesian network, which provides a description of the multisite/spatial dependence structure of the target locations; with an ensemble of conditional random forests, which extract the relevant information from the large-scale predictors to produce accurate probabilistic predictions. This work extends two previous articles that explored Bayesian networks (Legasa & Gutiérrez, 2020) and random forests (Legasa et al., 2022) in the context of statistical climate downscaling. Building on the experimental and validation framework proposed in the experiment 1c of the COST action VALUE (the largest, most exhaustive intercomparison study of statistical downscaling methods to date) and under the perfect prognosis approach, we thoroughly assess the performance of the proposed methodology focusing on its capability to reproduce several multisite and single-site statistics of the observed series. BNICRFs accurately capture the relevant spatial relationships while keeping the same single-site predictive performance than both single-site random forests and a Generalized Linear Model that exhibited a good performance in VALUE. Moreover, we compare BNICRFs against a robust multisite methodology proposed in Chandler (2020), obtaining better predictive capability while keeping similar spatial performance. Since the assessed models still underestimate autocorrelation, we also propose a straightforward extension of BNICRFs that incorporates the temporal structure at each location of interest able to produce both temporarily and spatially realistic precipitation occurrence fields

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short-period seismic sensors is installed on the surface on Mars as part of NASA’s InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one-sided pulses often accompanied by high-frequency spikes. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS-internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS’ raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars’ internal structure should benefit from deglitched seismic data.

A document by Sajjad Azimi. Click on the document to view its contents.

Recent activity has provided new insights into the causes of surface deformation in and around the Yellowstone Caldera, a topic that has been debated since the discovery of caldera-floor uplift more than four decades ago. An episode of unusually rapid uplift (>15 cm/yr) centered near Norris Geyser Basin along the north caldera rim began in late 2013 and continued until a Mw 4.9 earthquake on 30 March 2014; thereafter, uplift abruptly switched to subsidence. Uplift at rates of several cm/yr resumed in 2016 and continued at least through the end of 2018. Modeling of Global Positioning System (GPS) and interferometric synthetic aperture radar (InSAR) data suggests an evolving process of deep magma intrusion during 1996-2001 followed by volatile ascent and accumulation at shallow levels, perhaps as shallow as a few hundred meters depth. The preferred deformation model in which the volatiles accumulated is a shallow uplifted (domed) reservoir. The depth of shallow volatile accumulation appears to have shallowed from the 2014 to the 2016 deformation episode, from 3.2 km depth to 1.8 km depth respectively, and frequent eruptions of Steamboat Geyser since March 2018 might be a surface manifestation of this ongoing process. Hydrothermal explosion features are prominent in the Norris Geyser Basin area, and the apparent shallow nature of the inferred volatile accumulation might represent an increased risk of hydrothermal explosions in the vicinity of Norris Geyser Basin.

This study evaluates the performance of a global storm-resolving model (GSRM), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). We analyze marine boundary layer clouds in a cold air outbreak over the Norwegian Sea in a 40-day simulation, and compare them to observations from satellite and a field campaign of the Atmospheric Radiation Measurement program (ARM). SCREAM qualitatively captures the cold air outbreak cloud transition in terms of the boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also correctly locates the greatest ice and liquid in the mesoscale updraft. However, the study finds that SCREAM might underestimate cloud supercooled liquid water in the cumulus cloud regime. This study showcases the promise of employing high-resolution and high-frequency observations under similar large-scale conditions for evaluating GSRMs. This approach can help identify model features for future process-level studies before allocating extra resources for a time-matched model intercomparison of a specific case.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

Permafrost-affected ecosystems are prone to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences for otherwise N-limited tundra vegetation. Here, we show that the upper permafrost of the Tibetan Plateau is subject to thawing and that the upper permafrost zone is rich in ammonium. Furthermore, a five-year 15N tracer experiment showed that long-rooted plant species were able to utilize 15N-labeled N at the permafrost table and far below the main root zone. A 20 years survey is used here to document that long-rooted plant species had a competitive advantage at sites subject to warming and that both plant composition and growth were significantly correlated with permafrost thawing and changes in nitrogen availability. Our experiment documents a clear feedback mechanism of climate warming, which releases plant–available N favoring long-rooted plants and explains important changes in plant composition and growth across sites on the Tibetan Plateau.

Geodetic network infrastructure has evolved with increasing pace the past decade with remarkable additions of modern hardware, replacing aging, '80s vintage equipment throughout the globe. The Satellite Laser Ranging-SLR network is the slowest in making changes designed and planned more than a decade ago [Pearlman et al., 2019a]. This is in part due to the voluntary nature of establishing such installations and to a greater part the high cost and limited availability of the one-of-a-kind equipment. NASA, partners and international agencies, embarked on updates with standardization will help in the long term [Merkowitz et al., McGarry et al., Wilkinson, et al., 2019]. SLR needs more than updating the network to deliver the accuracy required today. New "targets" must also be used that support mm-accuracy. LAGEOS was conceived and built in the early '70s with a ~5 mm accuracy in mind [Pearlman et al., 2019b]. This limitation forced analysts to develop approaches of data analysis to ensure that even with such data one can reach the required 1-mm accuracy [Luceri et al., 2019]. Along with the network updates a parallel effort was thus initiated to modernize the space segment as well. Initially with the design and launch of LARES in 2012 [Pavlis et al., 2015] and following that, the design of LARES-2 [Ciufolini et al., 2017, Paolozzi et al., 2019], which was successfully launched on July 13, 2022 [https://www.nature.com/articles/d41586-022-02034-x]. The new mm-accurate target was quickly acquired first by the Matera, Italy station only three days after launch and although very early in the mission, the data were of remarkably high quality and insignificant bias. This prompted a quick evaluation and a test inclusion of this target in the limited list of SLR targets supporting the ITRF development. With an orbit nearly identical to LAGEOS (with supplementary inclination), taking full advantage of all the appropriate models designed and applied to LAGEOS, we achieved 7-day orbital fits of 3-5 mm even without a tuned target signature correction! We will present an overview of the initial analysis of LARES-2 data focusing on comparing them to contemporaneously taken LAGEOS data, we will show results from our initial inclusion of LARES-2 in developing ILRS products for ITRF development and discuss the ILRS plans for its full integration. Ciufolini, Phys. Rev. Lett (1986) Ciufolini, Int. J. of Mod. Phys. A (1989) Pearlman et al., J Geod 93, 2161-2180 (2019a). https://doi.

Stable isotopes of water are important tracers in hydrologic research for understanding water partitioning between vegetation, groundwater, and runoff, but are rarely applied to large watersheds with persistent snowpack and complex topography. We combined an extensive isotope dataset with a coupled hydrologic and snow isotope fractionation model to assess mechanisms of isotopic inputs into the soil zone and implications on recharge dynamics within a large, snow-dominated watershed of the Upper Colorado River Basin. Results indicate seasonal isotopic variability and isotope lapse rates of net precipitation are the dominant control on isotopic inputs to the basin. Snowpack fractionation processes account for <5% annual isotope influx variability. Isotopic fractionation processes are most important in the shrub-dominated upper montane. Effects of isotopic fractionation are less important in the low-density conifer forests of the upper subalpine due to vegetative shading, low aridity, and a deep, persistent snowpack that buffers small sublimation losses. Melt fractionation can have sub-seasonal effects on snowmelt isotope ratios with initial snowmelt depleted but later snowmelt relatively enriched in heavy isotopes through the isotopic mass balance of the remaining snowpack, with the efficiency of isotopic exchange between ice and liquid water declining as snow ablation progresses. Hydrologic analysis indicates maximum recharge in the upper subalpine with wet years producing more isotopically depleted snowmelt (1-2‰ reduction in d18O) through reduced aridity when energy-limited. The five-year volume-weighted d18O in this zone (18.2±0.4‰) matches groundwater observations from multiple deep wells, providing evidence that the upper subalpine is a preferential recharge zone in mountain systems.

NASA’s InSight mission records several high-frequency (>0.5 Hz) dispersive seismic signals. These signals are due to the acoustic-to-seismic coupling, where the infrasound is generated by meteorite entry and impacts. This dispersion property is due to infrasound propagating in a structured atmosphere, and we refer to this infrasound as guided infrasound. We propose to model the propagation of guided infrasound and the seismic coupling to the ground analytically; we use a 1D layered atmosphere on a three-layer solid subsurface medium. The synthetic ground movements fit the observed dispersive seismic signals well. We also examine and validate the previously-published subsurface models derived from the InSight ambient seismic vibration data.

Ice Floe Tracker is an open-source tool designed to retrieve floe-scale sea ice motion in the Arctic marginal ice zone during spring and summer.  Ice Floe Tracker enables observation of the floe size distribution, ice floe rotation rates, small-scale variation in floe velocity, and individual floe trajectories. Sea ice motion occurs on a wide range of scales, from the interaction of individual pieces of ice at sub-meter scales, the formation of linear kinematic features, and ice transport via basin-wide gyres. Most existing methods for tracking ice motion from remote sensing imagery rely on cross-correlation and are optimized for the winter season in the central Arctic. Cross-correlation-derived motion vectors estimate area-averaged motion and thus are well-suited for close-packed central Arctic ice; however, such estimates have high uncertainties in the dynamic, strongly deforming sea ice cover of the marginal ice zone. Our tool aims to fill this gap by using shape detection and feature tracking to observe floe-scale ice motion.The Ice Floe Tracker algorithm consists of a series of customizable modules. The code is structured as a modular package written in open-source languages. It includes parallel processing, unit testing, a command line interface, and thorough documentation (available on Github). Routines are provided to download imagery from the NASA Moderate Resolution Imaging Spectroradiometer. The satellite imagery is processed to enhance the contrast between liquid water and sea ice, sharpen floe boundaries, and remove atmospheric noise. The image is then segmented, and geometric features of ice floes are extracted. Finally, ice floe geometry and locations are compared to those in subsequent images and linked to form trajectories.  By making this tool open-source, we aim to encourage cross-disciplinary collaboration. Recent results from collaborations between observational oceanography and discrete-element sea ice model development will be highlighted. 

The Red Sea is an extremely warm tropical sea that hosts diverse ecosystems; thus, it is important to understand its ecology in the context of global warming. Using a coupled physical–biogeochemical model validated against in situ data, we provide the first report on the diel cycle (i.e., diel variability) in the Red Sea chlorophyll (CHL) concentration, revealing near-sunset CHL maxima at 17h ± 1h local time over the entire basin. This CHL peak time is considerably later than those reported in most other oceans, suggesting low grazing rates in this high-irradiance tropical sea. Model-based analyses reveal that CHL diel cycle is predominantly controlled by irradiance, whereas longer-timescale (e.g., seasonal) CHL variability is regulated by nutrient availability, suggesting a light-limited biological production at diel timescale. The identified CHL diel cycle comprises a fundamental component of the Red Sea ecology and has implications for CHL remote sensing and in situ measurements.

In this poster, we begin to explore how socio-geographical considerations can inform the development of data infrastructure, notably Persistent Identifiers. PIDs have become largely accepted within the Research Data Alliance, W3C, and elsewhere as core elements of data infrastructure. Science is comprised of many divergent formal and informal viewpoints at many different levels with a need for generalizable findings. PIDs act as “Boundary Objects” (Star & Griesemer, 1989) — objects that are part of multiple social worlds and facilitate communication between them. They allow meaning to be understood in different contexts and are “plastic enough to adapt to local needs, … yet robust enough to maintain a common identity across sites. They are weakly structured in common use and become strongly structured in individual site use.” Boundary objects work to reduce local uncertainty without damaging cooperation. It is a question of re-representations across intersecting worlds not consensus. PIDs work to allow machines and humans to understand which digital object is in question (identity), what it is (type), and where it is (location). Each of these questions is surprisingly fraught and complex.

Australian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions from Australia remain highly uncertain. In this work, we use surface in situ, ground-based total column and satellite total column observations to evaluate the ability of two global models (GEOS-Chem and ACCESS-UKCA) and three global biomass burning emission inventories (FINN1.5, GFED4s, and QFED2.4) to simulate carbon monoxide (CO) in the Australian atmosphere. We find that emissions from northern Australia savanna fires are substantially lower in FINN1.5 than in the other inventories. Model simulations driven by FINN1.5 are unable to reproduce either the magnitude or the variability of observed CO in northern Australia. The remaining two inventories perform similarly in reproducing the observed variability, although the larger emissions in QFED2.4 combined with an existing high bias in the southern hemisphere background lead to large CO biases. We therefore recommend GFED4s as the best option of the three for global modelling studies with focus on Australia or the southern hemisphere. Near fresh fire emissions, the higher resolution ACCESS-UKCA model is better able to simulate surface CO than GEOS-Chem, while GEOS-Chem captures more of the observed variability in the total column and remote surface air measurements. We also show that existing observations in Australia can only partially constrain global model estimates of biomass burning. Continuous measurements in fire-prone parts of Australia are needed, along with updates to global biomass burning inventories that are validated with Australian data.

Understanding what causes weather-related stresses that lead to crop failures is a critical step towards stabilizing global food production. While there are many sources of weather-related stresses, the 30-60 day Madden-Julian Oscillation (MJO) is the dominant source of subseasonal climate variability in the tropics, making it a potential – but as of yet unexplored – source of crop failures. Here crop models and observational yield statistics are used to assess whether the MJO affects maize yields. We find that the influence of the MJO is widespread, affecting crop yields throughout the tropics. In dry, hot environments the MJO can lead to crop failures by reducing precipitation, decreasing soil moisture, and increasing extreme heat, while in wetter, cooler environments - where water stress is less common - MJO-forced decreases in rainfall bring increases in solar radiation that benefits crop yields. These results provide a pathway to develop actionable early warnings using subseasonal forecasts.