Soil moisture is a vital climatic variable driving environmental and biological processes. Large scale soil moisture can be estimated using Land Surface Models or observed using active and passive microwave remote sensing. Increasing availability of remotely sensed soil moisture retrievals has allowed for constraining Land Surface Model (LSM) estimates. Though the accuracy of remote sensing datasets is constrained by soil roughness, vegetation, and surface temperature, combining them with LSMs allow us to reduce errors in soil moisture estimates. This study assimilates the SMOPs-ASCAT, ESA-CCI and SMAP soil moisture retrievals into a land surface model u sing an ensemble Kalman filter. The open-loop and data assimilated soil moisture outputs are evaluated against the ground-based sensor data. This demonstrates the establishment of an Indian Land Data Assimilation System (ILDAS) with the goal of providing accurate soil moisture products at high spatial and temporal resolution over India.

In this work, the characteristics of the diurnal cycle around Sumatra are examined with unprecedented detail using high-resolution satellite-derived cloud properties from the Himawari-8 Advanced Himawari Imager (AHI) data. Offshore propagation is resolved into multiple local propagation directions of cloud cells, and the interaction between propagations is shown to result in forced convection over the Strait of Malacca, Java Sea, and the Indian Ocean. The diurnal cycle of rainfall and deep convection over Sumatra show complex interactions between the land-sea-breeze system, the seasonal background wind, the local topography, and the influence of surrounding islands. We used high-resolution satellite-derived products from Himawari-8 AHI, the Geostationary Cloud Algorithm Testbed Geocat, and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) to investigate the cloud properties of deep convection and the signatures of the diurnal cycle of rainfall and cloudiness over Sumatra. Previous studies have shown evidence of the variability of diurnally forced convection in the Maritime Continent, including the diurnal signal over land in the late afternoon and the offshore propagation of Sumatra at night (Yang and Slingo 2001). The role of gravity waves has explained the night-time propagation (e.g., Mapes 2003; Love et al. 2011; Vincent and Lane 2016; Sakaeda et al. 2020). This propagation can be modified by the influence of small islands and the interconnection of diurnal cycles between Sumatra, Malay Peninsula, Java, and Borneo (Ruppert and Zhang 2019; Ruppert and Chen 2020). In this work, we present evidence of the cloudiness and rainfall patterns propagating offshore/onshore Sumatra during five austral summers from 2016 to 2020, employing cloud properties from Himawari-8 and IMERG collections. By combining detailed satellite-based cloud properties and rainfall estimates, we highlight the strong dependency of the diurnal cycle on local modulators.

The Ocean Worlds Life Surveyor (OWLS) is a field prototype instrument suite designed to autonomously search for evidence of water-based life, developed in preparation for potential future missions to ocean worlds such as Enceladus and Europa. One instrument included in this suite is a Capillary Electrophoresis-Electrospray Ionization Mass Spectrometer (CE-ESI MS), which can detect the presence of organic molecules and other potential biosignature compounds. Due to the extreme energy costs involved in communication from these distant worlds, a mission’s downlink bandwidth is insufficient to return raw data from even a single recorded dataset. We developed two onboard capabilities to address this constraint: compression via knowledge summarization, and prioritization for the most scientifically useful observations. To summarize and prioritize the data generated by the CE-ESI MS, we developed the Autonomous CE-ESI Mass-Spectra Examination (ACME) system. ACME performs content summarization while ensuring that scientifically valuable signals are retained. First, ACME identifies and characterizes potential peaks in the mass spectra, each of which may indicate the presence of a specific compound. Then, ACME uses a decision tree model trained on expert-labeled data and peak properties such as width and signal-to-noise ratio to filter only for peaks of likely scientific interest. Finally, ACME produces a series of Autonomous Science Data Products (ASDPs): crops of small regions of the raw mass spectra data around each peak, a summary of the background noise to provide context and justification for its decisions, estimates of the scientific utility of the observation, and a brief description of its contents to enable downlink prioritization based on known science targets of interest as well as diversity sampling. Typical data sizes of the peak locations, crops, and background noise summary satisfy the mission downlink bandwidth constraints with an average compression ratio of 900:1. ACME was validated on lab- and field-collected data to confirm that scientists are able to successfully analyze and make valid scientific conclusions using only ACME’s ASDPs, compared to analyzing the raw data directly.

Solar-Induced Chlorophyll Fluorescence (SIF) is a powerful proxy for gross primary productivity (GPP) in Boreal ecosystems. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. Recent work has highlighted non-linearities between SIF and GPP at finer spatial (leaf- to canopy- level) and temporal (half-hourly) scales. Therefore, questions have arisen about when, where, and why SIF is a good proxy for GPP and what the potential sources for divergence between the two are. The goal of this study is to answer two specific questions: 1) At what temporal scale is SIF a good proxy for GPP and 2) What are the predominant physical and ecophysiological drivers of nonlinearity between SIF and GPP in boreal ecosystems? We collected tower-based measurements of SIF (and other common vegetation indices) with PhotoSpec (a custom spectrometer system) and eddy-covariance GPP data at a 30-minute resolution at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA. We applied a combination of statistical and machine learning approaches to disentangle the influence of structural/illumination effects and ecophysiological variations on the SIF signal. Our results show that at a high temporal resolution (half-hourly), SIF and GPP are predominantly dependent on photosynthetically active radiation (PAR). Therefore, the non-linear light response of GPP drives non-linearity between SIF and GPP. Additionally, canopy structure and illumination effects become important to the SIF signal at high temporal resolutions. At the seasonal timescale, SIF and GPP exhibit co-varying responses to PAR, even when accounting for changes in canopy structure. We attribute changes in the light responses of SIF and GPP to sustained photoprotection over winter which co-varies with changes in temperature. Finally, we show that the relationship between SIF and GPP has a seasonal dependence caused by small differences between the light use efficiencies of fluorescence and photosynthesis. Accounting for this seasonally variable relationship will improve the use of SIF as a proxy for GPP.

Creeping faults are typically not associated with large earthquakes. However, new K/Ar dating and biomarker maturity data on the San Andreas Fault Observatory at Depth (SAFOD) present evidence that large paleoearthquakes have occurred in the creeping section of the San Andreas Fault, California. K/Ar ages of bulk samples with evidence of coseismic heating range from 3.3 to 15.8 Ma, and argon diffusion experiments suggest that these ages are only partially reset and the actual event ages may be even younger. Thus, questions remain as to how we can refine such dates to reveal the precise age and location of these earthquakes. To refine the ages and more accurately assess seismic hazard, we date size separates of eight samples from different sections of the SAFOD core. Following Stokes’ Law, we split each sample into five size fractions using hydrodynamic settling: <0.2, 0.2-0.5, 0.5-0.8, 0.8-1.4, and 1.4-2 micrometers. The finest size fractions contain the most authigenic illite, which form during fault slip. We determined chemical composition and separated illite polytypes using x-ray diffraction, and also measured K/Ar ages on each sample. Preliminary results from two scaly black fault rock samples, previously shown to have hosted earthquakes, (3,193.69 m and 3,193.96 m along the core) support that the finest size fractions contain the greatest ratio of authigenic illite. With a York regression between age and detrital illite abundance, we place the authigenic illite ages at 1.08 ± 2.40 Ma and 0.88 ± 5.08 Ma for these two samples, and observe that the detrital illite matches the late Cretaceous age for the country rock. This new age estimate for the authigenic illite means that large earthquakes must have propagated into the creeping section within the last million years. Not only is it significantly younger than the bulk sample age, it is recent enough that translation of faulted material from the locked southern San Andreas fault into the creeping section cannot explain the record. Moving forward, we will expand our procedure to include isotope dilution for measuring K concentration and analyze the other samples previously measured for biomarker maturity and bulk K/Ar age. Resulting insights into the fault rock composition and the timing of past earthquakes will be crucial in assessing the region’s seismic hazard.

In this presentation, we seek to understand the altitudinal response of the auroral E-region neutral wind to substorms using observations from the Poker Flat Incoherent Scatter Radar (PFISR). Zou et al. (2009) presented different nighttime ion drift pattern associated with three different types of substorm events whose onset locations were at, to the east and to the west of PFISR. In addition, Zou et al. (2021) presented different types of latitudinal responses of the F-region neutral wind to substorm events and suggested that the response is highly local time dependent. While these two studies give much insight on the F region ion and neutral responses during substorm events, the E region neutral wind response is not well-understood. Previous studies showed that during disturbed conditions E-region winds, in particular below 110 km, are larger than what tidal theory predicts, while the winds in the upper E-region shows strong impacts from the convection flow. We select events whose onset locations are over the PFISR radar site, but in different MLT sectors using the SuperMag data product during 2010-2019. We present a superimposed epoch analysis of three different types of events after subtracting the quiet background neutral winds. This study will further our understanding of the contribution in a statistical view from forces that are associated with the E-region wind variation during substorm events during different MLT sectors and spanning the altitude range from 90-130 km.

In tropical islands, interactions between atmospheric circulation and island topography generate complex patterns of orographic rain. Despite recent advances, numerical weather models are still challenged by orographic rain enhancement, in particular because the physical processes responsible for rain generation over tropical islands vary at spatial and temporal scales that are not yet fully resolved in these numerical models. A large part of our knowledge about tropical island precipitation patterns therefore relies on rain observations. However, observing strongly variable rain fields is not an easy task. It requires instruments able to capture rain fluctuations at fine temporal (1 min - 1h) and spatial (100 m - 1 km) scales. To reach these requirements, state of the art rain observation methods rely on remotely sensed weather radar images and in-situ rain gauge measurements, or solely on rain gauge observations in the many areas like Tahiti where radar observations are not available (mainly due to financial cost). In this study we investigate to which extent a temporary densification of the rain monitoring network of Tahiti (lon = 149.5°W, lat = 17.6°S, area = 1042 km2, max altitude = 2241 m) can help us gaining new insights about orographic rain enhancement on a high altitude tropical island with complex topography. To this end, 10 low-cost but high resolution rain gauges have been deployed for one year (Aug 2020 - Aug 2021) in addition to the long term network of around 20 rain gauges operated by the Direction de l’Equipement Tahiti (Groupement d’Etudes et de Gestion du Domaine Public) and the French Weather Service (Météo France Polynésie). Based on this new dataset, we first characterize the space-time patterns of orographic rain enhancement over Tahiti at an unprecedented resolution, and link these emerging patterns to local features of the atmospheric circulation. Next, we investigate the added value of our temporary network for purposes of rain mapping (i.e., spatial interpolation of point observations). Finally, we explore the benefits and limitations of temporary rain gauge network densification for orographic rain enhancement observation.

Flooding, moisture, and climate combined with traffic conditions are responsible for the failure of pavement and traffic disruption. Flooding may deteriorate the structural integrity of the road foundation resulting in huge rehabilitation and maintenance costs. Estimating flooding extent and flow characteristics is, therefore, key to assessing the impact of inundation on road links and networks in low-lying areas that are frequently exposed to flooding water. Such an assessment is helpful for decision-makers in pavement design, rescue operations, and infrastructure planning. Flooding from heavy precipitation is the primary weather-related hazard to transportation infrastructure in the state of Louisiana. Quantification of flood inundation area and depth is essential to quantify the exposure of infrastructure such as pavement and traffic flow to flooding in the changing climate. Two-dimensional hydraulic models that use the governing equation of flow to simulate flow characteristics are widely used to simulate flood extent and depth. However, simulating hydraulic models at acceptably satisfactory resolution and for large spatial extension may require substantial effort. More recent methods have used satellite remote sensing to characterize the extent of the flood, but satellite data still have limitations in producing reliable flood depth information. Modeling framework based on hydraulics and machine learning provides efficient large-scale flood simulation applications. This study tested the GIS tool, the Flood Depth Estimator (FwDET), to estimate complex inundation patterns in an urban setting. When driven by the LiDAR Digital Elevation Modeling and flood extent map, the tool can provide a rapid assessment of the spatial distribution of depth in the flood-prone region and state highway of Amite and Comite Rivers, Louisiana.

Understanding the near-surface roughness is paramount for the evolution of surface features and its contribution to the ice sheet dynamics. In regions experiencing the transition from dry snow to melt conditions, the surface becomes rough which along with liquid water content influences the radar backscatter. Such surfaces are driven by wind resulting in the formation of features oriented at different azimuth directions, ranging from ripples to dunes. Different scales of roughness also induce depolarisation in the radar signal, thereby making it difficult to infer the effective contribution of densification process on total backscatter. Here, we present a physical understanding of a wind-induced snow surface having anisotropic roughness from the modelled radar backscatter. As part of the approach, we propose to model the horizontal component of roughness (autocorrelation length) as a function of azimuth direction in the surface scattering model, i.e. Integral Equation Model. We performed our experiments for strongly anisotropic (a < 0.2), weakly anisotropic (a > 0.7), and isotropic (a = 1) surfaces, where a is the anisotropy, tuned for C-band frequency. Also, the minima of backscatter intensity varied over entire range of azimuth angles at a particular incidence angle is derived for retrieving the most dominant wind direction. From our analysis, significant azimuth modulation occurs in the radar backscatter at low incidence angles. Moreover, we found that the most dominant wind direction at 30-deg incidence angle changes from a strongly anisotropic surface (i.e. 0-deg) to a weakly anisotropic surface (i.e. 90-deg), however it remains unaltered for 45-deg and 60-deg incidence angles (i.e. 90-deg). Our modelled results are in strong agreement with wind scatterometer data of ice sheets and further show the potential of 30-deg incidence angle for tracking the changes in wind flow pattern based on directionality of roughness. In this regard, we foresee the importance of azimuth modulation for understanding the properties and orientation of surface features in the ice sheets, with particular application to wind flow. As a future scope, we plan to apply our model on radar images for the retrieval of wind directions and compare the directionality of roughness with the katabatic wind flow patterns and AWS measurements.

Despite methane’s important role as a greenhouse gas, the contribution of individual sources to rising methane concentrations in Earth’s atmosphere is poorly understood. This is in part due to the lack of frequent measurements on a global scale, required to accurately quantify fugitive methane sources. Future missions such as Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and Carbon Mapper promise to provide global, spatially resolved spectroscopy observations that will allow us to map methane sources. However, the detection and attribution of individual methane sources is challenged by retrieval artifacts and noise in retrieved methane concentrations. Additionally, manual methane plume detection is not scalable to global space-borne observations due to the sheer volume of data generated. A robust automated system to detect methane plumes is needed. We evaluated the performance and sensitivity of several methane plume detection methods on 30m to 60m hyperspectral imagery, downsampled from airborne campaigns with AVIRIS-NG. To aid the training of the plume detection models, we explored supplementing downsampled airborne imagery with Large Eddy Simulations (LES) of methane plumes. We compared baseline methods such as thresholding and random forest classifiers, as well as state-of-the-art deep learning methods such as convolutional neural networks (CNNs) for classification and conditional adversarial networks (pix2pix) for plume segmentation.

The lifetime of excess atmospheric CO2 (above preindustrial) xCO2 governs the future consequences of present xCO2 and those of future CO2 emissions. Recent assessments of the decrease of xCO2 following abrupt cessation of anthropogenic emissions (zero emissions commitment, ZEC) inferred from studies with carbon-cycle (CC) models (e.g., Joos et al., ACP, 2013; MacDougall et al., BG, 2020) vary substantially, with the fraction of xCO2 remaining in the atmosphere 100 years after cessation, fCO2(100) = xCO2(100)/xCO2(0), ranging from 0.55 to 0.85 (Figure 1a; Schwartz, JGR, 2018; Schwartz, in review). In this study prior atmospheric and oceanic CO2 and future xCO2 for ZEC were calculated with a 5-compartment global model. Model compartments are the atmosphere, upper and deep ocean, and labile and obdurate terrestrial biosphere (TB). Model parameters are obtained mainly from observation (e.g., rate of uptake of heat by the deep ocean) and theory (e.g., CO2-dependent solubility of CO2 in seawater); uptake of CO2 by the two TB compartments is apportioned by parameterization, with parameters rather narrowly constrained by observations of CO2 and radiocarbon. CO2 is found to decay much more rapidly than in CC models; fCO2(100) = 0.41 ± 0.8 (1 σ), Figure 1a. These results indicate that cessation of anthropogenic CO2 emissions would result in discernible decrease in atmospheric CO2 on a time scale as short as a human lifetime, much faster than in current CC models. Shown in Figure 1b is a quantity denoted τE(t), the equivalent 1/e lifetime of xCO2, as a function of time subsequent to cessation of emissions t, evaluated as τE(t) = -1/ln fCO2(t). τE(t) is a generalization of the relation between half-life of a decaying quantity and its 1/e lifetime and is an integral measure of decay over time t. The present model yields τE(t) of excess atmospheric CO2 about 100 years, much shorter than obtained with current CC models. Figure 1. a, Fractional excess CO2 fCO2(t) as function of time t following abrupt cessation of anthropogenic CO2 emissions as calculated in a recent model intercomparison (MacDougall et al., 2020) and with present model (best estimate, thick red, and uncertainty range); dotted black lines denote exponential decay with lifetime indicated at right. b, Equivalent 1/e lifetimes as function of t.

Hydroclimate extreme events, especially precipitation and streamflow, pose serious threats to life, livelihoods, and infrastructure. However, the extremes exhibit significant space-time variability and in conjunction with societal vulnerability and resiliency, resulting in varying levels of damage. Regardless, robust understanding and modeling of these extremes is crucial for effective hazard mitigation strategies. For this study, we focus on the Krishna River Basin in south India, which experiences flooding each year due to monsoon rains and impacts urban and rural communities along its network covering three States. We implement a Bayesian hierarchical model to capture the spatio-temporal variability of streamflow extremes on this river network. In this model, the extremes (3-day maximum seasonal flow) at each station are assumed to follow a Generalized Extreme Value (GEV) distribution with non-stationary parameters. The parameters are modeled as a linear function of suitable covariates. In addition, the spatial dependence of the streamflow extremes is modeled via a Gaussian copula. With suitable priors on the parameters, posterior distribution of the parameters and the predictive posterior distribution of streamflow (i.e., ensembles) at each location. Consequently, various return levels can also be obtained from these ensembles. We developed and tested the model on the monsoon seasonal 3-day max flow at 10-gauge stations for the period 1973 -2015. To find the covariates, we perform analysis to identify relationships between large-scale climate variables such as Sea Surface Temperatures, 850 mb winds, Sea Level Pressure, etc. Statistical learning methods will be employed for this analysis and as a result, obtain potential covariates that best relate to streamflow extremes in the basin. This modeling approach can be adapted to the seasonal and multidecadal projection of extremes, which will greatly help disaster mitigation planning efforts.

Sexual harassment in STEM continues to be a pervasive barrier to women’s full participation in the sciences. Many studies conclude that workplace culture and lack of clear policies and practices exacerbate the risks of sexual harassment. Remote research environments, such as field stations and ocean platforms, bring additional risk to researchers. Participants already face acute safety concerns related to the remoteness of the field station or oceanographic vessels, fewer and less clear policies and enforcement regulations are in place, and multiple institutions bear responsibility, leading to a challenging environment for preventing and handling incidents. This workshop explored the factors that permit sexual harassment in remote research, and aimed to develop practices to prevent and respond to harassment in the field. The California State University Desert Studies Center and the Center for Ocean Leadership convened workshop in March, 2021 to address sexual harassment in field science. Over three days, field and ocean science leadership and practitioners came together with leadership from professional societies and academia, and experts in sociology, policy, and social justice. The goals were to: 1) open a dialogue between sexual harassment experts and the field research community to develop best practices and recommendations; 2) build coordination and consistency in policy setting and enforcement across field stations and oceanographic platforms; 3) develop processes to monitor the reporting of sexual harassment instances occurring at remote field locations; and 4) promote a safe culture for scientists conducting research at remote field stations and on oceanographic vessels. The workshop compiled and developed best practices and recommendations in four key areas: 1) culture change, 2) policy, 3) accountability, and 4) reporting. These recommendations were targeted at all facets of field and ocean sciences, from academic and research institutions, professional societies, and funding agencies, to departments and field research crews. Here we will give an overview of the workshop findings, with particular focus on the recommendations for research leadership.

Overallocation, increased demands, recognition and quantification of environmental flows, and climate change have combined to make water leasing a practical solution for addressing water shortages in many watersheds. Leasing involves the temporary transfer of the consumptive use portion of a water right to another user presumably for a higher value purpose. Therefore, understanding the effect of water leasing on river flow availability and river ecosystem protection is critical for the watershed-scale water management. Numerical and computational watershed simulation techniques coupled with water right models can allow water managers to allocate water based on the legal framework within the watershed, which in the western United States is typically based on the prior appropriation doctrine. In this study, we simulate the water trades on instream flow using an ASCE Penman-Monteith method of evapotranspiration estimation on water right model. Using the Touchet River basin in Washington State as a study location, we successfully demonstrated simulation of water leases on instream flow. We choose the Touchet River basin as a study location as this basin suffers frequent water curtailment and has existing partnerships across diverse sectors including water leasing markets to address this water resources concern. This information will be extremely valuable for river ecosystem protection and to watershed managers trying to plan future water managements, water storage, and water trading projects.

Earth’s transient climate sensitivity Str is the rapid change, plateauing at ~5 yr, in global mean surface temperature GMST per change in forcing (e.g., Held et al., JGR, 2010). Str is readily evaluated from time series of total forcing F and temperature anomaly ΔT as the slope of a regression of ΔT vs F, with ΔT from model or observations and F generally modeled based on change in atmospheric composition. Prior estimates of Str have varied quite widely, mainly because of uncertainty in aerosol forcing. F is evaluated as total non-aerosol forcing, dominated by positive GHG forcing, plus negative aerosol forcing; large magnitude aerosol forcing results in small F and in turn high Str, and vice versa. Forcing time series derived from the Fifth IPCC Assessment Report (AR5, 2013) resulted in best estimate Str 0.35 K (W m-2)-1; 5% to 95% uncertainty range 0. 27 to 0.55 (Schwartz, JGR, 2018). New time series of total forcing from the (2021) Sixth IPCC Assessment report (AR6) permit similar evaluation of Str, Figure 1, as 0.46 (0.36 to 0.50) K (W m-2)-1. The increase in best-estimate Str is due to increased magnitude of best-estimate aerosol forcing in AR6 vs. AR5. Poor long-term correlation of forcing time series and observed ΔT for the 5% forcing estimate (large negative aerosol forcing added to GHG forcing, yielding low total forcing) suggests that that the corresponding bound on aerosol forcing magnitude may be an over-estimate, with the correlation substantially improved for best estimate and even more so for lowest estimate of aerosol forcing magnitude, thus more consistent with lower values of Str. A somewhat higher range of Str, 0.42 to 0.75 K (W m-2)-1, is obtained using time series of forcings obtained with individual models (Smith et al., ACP, 2020). Figure 1. Time series of total forcing F and as convolved with 5-year decaying exponential Fc (left); correlations of observed temperature anomaly ΔT (GISS) vs Fc (center); slope denotes transient sensitivity Str; and time series of ΔT (right; left axis) and Fc (right axis, scaled to ΔT by Str). Top row, lower 5% bound on forcing time series; middle row, best estimate forcing; bottom row, 95% bound. Forcing data from draft AR6 report, expected release August 9, 2021, potentially subject to change.

Article 6 of the Paris Agreement on greenhouse gases enables countries to cooperate in implementing their nationally determined contributions (NDCs) towards emission reduction. Current national policies may fail to deliver on the “well below 2℃” climate goal & international cooperation through carbon markets under Article 6 is expected to enhance flexibility in mitigation options, make it cost-effective and enhance mitigation ambition overall. As countries prepare for the Glasgow Conference of Parties (COP26), the rules for such mechanisms are expected to be finalized. We analyze three questions: What is the aggregate & spatial distribution of economic efficiency gains & financial flows between Global North & South with Article 6? What is the impact of limits on inclusion of nature-based solutions? What are the multisectoral dynamic effects on technology deployment & capital investment in the Global South? We use the GCAM (Global Change Analysis Model) integrated assessment model & an 8-scenario matrix with two emission trajectories achieving carbon neutrality based on equity principles. In each case, we measure the geographic distribution of economic gains till 2050 between a pathway where countries move independently or participate cooperatively in a global carbon market. We analyze the spatial and multisectoral impacts on electricity asset stranding & investments in different mitigation options, including CCS (carbon capture & sequestration), electric vehicles, energy efficiency & renewable energy technologies. Employing limits on land area engaged for mitigation from fossil fuel sources, we showcase the sensitivity of these results to nature-based solutions. We find that in contrast to the popular notion that the Global South will inevitably gain through such global carbon market transfers, the story is more intricate. There is also a noticeable impact of limiting nature-based solutions for countries in Latin America & Sub-Saharan Africa. Further we show differences in deployment of low-emissions technologies in the near term. This has implications for technological growth & incentives for mitigation. This study provides insights for design of future global markets or regional carbon clubs & shows equity tradeoffs in achieving economic efficiency in climate change mitigation.

The Earth’s magnetosphere is filled by particles from two sources: the solar wind and the ionosphere. Ionospheric ions are initially cold and contain He+ and O+, in addition to to H+. Depending on their initial magnetic latitude and local time, and the state of the magnetosphere, they may contribute to the plasmasphere, the plasma sheet, the ring current, the warm plasma cloak etc. Depending on which path they follow in the magnetosphere, some of these ionospheric ions remain cold when they reach the two key reconnection regions: the Earth’s magnetopause and the plasma sheet in the tail. In this presentation, we will first review previous statistical works that quantify the number of cold/ionospheric ions near these two regions. Several works have attempted to quantify these populations, but they are inherently difficult to characterize due to their low energy, often below the spacecraft potential. We will also discuss the impacts they have on the magnetic reconnection process. Ionospheric ions mass-load the regions where reconnection takes place and change the characteristic Alfven speed, resulting in a smaller reconnection electric field. They also take a portion of the energy that is imparted to particles, affecting the energy budget of magnetic reconnection. Finally, they introduce new length and time scales, associated to their gyroradius and gyroperiod. We will discuss what are the implications of these impacts for the evolution of the magnetosphere – solar wind interactions.

The geodetic measurements of accumulated strains along active faults during their interseismic periods have a strong connection with faulting dynamics and seismic hazards evaluation. InSAR has been widely applied to study the interseismic deformation along active strike-slip faults around the world. However, various limitations such as that from phase unwrapping errors and tropospheric delays are often encountered, hindering our interpretation and model inversion. Phase-gradient stacking is a method that sums up wrapped phase differences of adjacent pixels in multi-interferograms. It has been successfully conducted to reveal local deformation signals across coseismic fractures, yet lacks of application to relatively larger-scale deformation signals. Here we apply the phase-gradient stacking method, for the first time, to study the interseismic deformation along the North Anatolian Fault with Sentinel-1 SAR images acquired from 2014 to 2021. We obtain the strain rate field across the North Anatolian fault without the need of unwrapping hundreds of large interferograms. Segments with surface creep and strong coupling effects can be clearly distinguished in the phase gradient maps, allowing us to directly invert for their long-term slip rates and locking depths. Our preliminary, but promising results show that the phase-gradient stacking method has advantages in studying interseismic deformation along strike-slip faults by directly connecting strain with fault parameters.