Tomographic analysis of Pn arrivals times—the guided P-wave propagating within the lithospheric mantle—is ideal for studying the structure of the uppermost mantle. While plate-scale seismic images of Pn wave speeds are common beneath the continents, similar scale studies have not been possible within ocean basins due to the sparsity of seismic stations. The Cascadia Initiative (CI) dataset provides the first opportunity to image spatial variations in lithospheric structure across an entire oceanic plate. We measure 2,862 Pn arrivals from local earthquakes recorded by the CI array. Our dataset provides complete coverage of both the Juan de Fuca (JdF) and deforming Gorda plates. We invert the measured arrival times for 3D variations in anisotropic P-wave velocity and hypocentral parameters. Despite surficial evidence of extensive active faulting, the velocity structure of the Gorda uppermost mantle is remarkably consistent with predictions from a conductive cooling model (attached figure). Limited deformation at mantle depths is supported by seismic anisotropy measurements that show the fast-direction of P-wave propagation rotates in concert with the magnetic anomaly lineations. This rotation may be explained by local plate kinematics without internal deformation and hydration of the shallow mantle. In contrast to Gorda, the seismic velocity structure of the JdF plate does not exhibit a clear age dependence. Three pronounced mantle low-velocity zones are found along the southern edge of the JdF plate near the termini of large pseudofaults that contributed to the formation of the Blanco Transform fault. We attribute these velocity reductions to mantle alteration by seawater. We note that within the interior of the JdF plate pseudofaults do not appear as uniformly slow features in our seismic images. Beneath the central and northern JdF plate, P-wave speeds are ~7.7 km/s out to ~4-5 Myr before abruptly increasing to ~7.9 km/s. Curiously, this transition occurs near the onset of mantle downwelling inferred from teleseismic body wave tomography and attenuation suggesting that mantle flow dynamics may influence the structure of young oceanic lithosphere. Lastly, we note that our results do not suggest a relationship between the structure of the uppermost slab mantle and segmentation of the Cascadia megathrust.

We present a new statistical interpolation method to estimate fire perimeters from Active Fires detection data from satellite-based sensors, such as MODIS, VIIRS, and GOES-16. Active Fires data is available at varying temporal and spatial resolutions (375m and up several times a day, or 2km every 15 minutes), but pixels are often missing due to clouds or incomplete data. The question arises how to fill in the missing pixels, which is useful, e.g., to distinguish in an automated fashion between a single large fire visible as separate clusters of detection pixels because of cloud cover, and separate fires. We process the satellite data into information when was fire first detected at a location, and when was clear ground without fire detected at the location last. We are then looking for the most likely fire arrival time, which satisfies such constraints. Models at various levels of complexity are possible. Our base assumption in the absence of information to the contrary is that the fire keeps progressing without change, which is expressed as the assumption that the gradient of the fire arrival time is approximately constant. The method is then formulated as an optimization problem to minimize the total change in the gradient of the fire arrival time subject to the constraints given by the data. We consider probabilistic interpretations of the method as well as extensions, such as soft constraints to accommodate the uncertainty of the detection and the uncertainty where exactly the fire is within the pixel. This method is statistical in nature and it does not use fuel information or a fire propagation model. The results are demonstrated on satellite observations of large wildfires in the U.S. in summer 2018 and compared with ground and aerial data.

Precise understanding of complex physical mechanisms of mesoscale process require high resolution observations of temperature, moisture, wind, precipitation, clouds. Using all collocated observations of microwave radiometer, wind profilers, electric field mill, weather radars over South-East India an observational analysis is conducted for the first time. Analysis suggests that these systems developed in warm, moist environment associated with large-scale low-level convergence. Passage of system is accompanied by convective regions with intense upward motion and towers extending up to higher levels indicating developing phase and presence of upward/downward motion comprising of heavy precipitation representing mature phase of the system followed by stratiform regions with prominent downdraft motion and less precipitation related to decay phase. Large (small) values of reflectivity and cloud liquid water values represent presence of deep (shallow)convective (stratiform) regions. Cloud to Ground (CG) lightning activity associated with storm electrification processes showed the existence of both +CG and –CG flashes in convective and dominant –CG in stratiform regions. Presence of different sized cloud liquid hydrometers in convective regions resulted in bipolar nature due to their collisions however in stratiform regions their distribution is mostly uniform and resulted in single polarity. Combination of different observations has provided the unique opportunity to examine interrelations of different physical mechanisms in storm environment. Inspection of reflectivity, CG lightning and cloud liquid water measurements have demonstrated the relationship of lightning mechanism with storm dynamics and cloud microphysics. Combined investigation of temperature, moisture and wind measurements have given considerable insight of thetae ridge formations resulting from thermal and moisture advections. Isentropic upgliding and downgliding facilitated the unique way to visualize the vertical transport of temperature and moisture through ascent and descent of air parcel. Blend of observations presented considerable insight of synoptic and complex mesoscale processes and their mutual interactions in the storm environment and provided encouraging results in explaining MCS structure.

The increased availability of daily model output in the latest generation of climate models should allow us a greater understanding of, and an improved ability to predict, future trends in short duration extreme temperature events. We examine the changes in 1, 3, and 5 day averaged annual maximum temperature at the levels of global warming highlighted by the 2016 Paris Agreement, and an additional degree of warming. These events are characterised using daily near surface air temperature output from four large ensemble models in the SSP370 scenario of the Coupled Model Intercomparison Project Phase Six (CMIP6). Bootstrapped distributions of ensemble members are fitted to the generalised extreme value distribution and the changes in location, scale and shape parameters examined at the respective warming levels, compared to the last 10 years of historical model runs. Global trends in location parameter indicate increased warming over land relative to oceans while shape and scale parameters show less globally consistent trends but very clear signals of strong changes over the Arctic sea ice. Risk ratios are determined for extreme temperature events with a return value of 10 years in the historical period, compared to future levels of warming, using the calculated GEV distributions. Risk Ratios increase globally with mean temperature change, with greater increases over the tropics, particularly over the oceans. Longer events are also found to have a greater increase in occurrence than shorter duration temperature extremes.

Open, interdisciplinary science inevitably relies heavily on standards. Standards are those often unseen agreements that we take for granted when systems and processes are working fine. Yet standards work is perpetual, laborious, and sometimes contentious, especially for standards to work across diverse disciplines. Standards development, maintenance, and implementation is a complex, ongoing socio-technical process. NASA has developed a progressively open science policy and strategy that calls for the establishment of a data standards process reaching across the five diverse divisions of the Science Mission Directorate. This is a delicate exercise. We, therefore, seek to apply a holistic yet pragmatic approach to developing and maintaining a standards process. We adopt an ecological philosophy that focuses on the interactions within the data ecosystem and how standards facilitate those interactions. We couple high-level analysis with on the ground experimentation. We began by 1) mapping information ecosystem components (e.g. data centers, missions, services, protocols, users), 2) establishing how the components interact (e.g. sharing (meta)data, funding, personnel exchange), and 3) modelling system dynamics (e.g. creation of products from multiple data centers, redundant processes, shared services). The goal is to apply understanding of the ecosystem to real world applications (e.g. planning a new mission, implementing new policy requirements, improving process efficiency, etc.). We have also conducted studies of historical standardization efforts, documenting lessons learned and cautionary tales. We then contrast this more abstract work with real examples. We reviewed and assessed multiple existing standards development processes both within and external to NASA. We now work to implement an initial test process which can be further optimized. We seek to define a consistent approach for assigning persistent identifiers for research objects, especially for the purposes of citation. The experience from this relatively ‘simple’ test case adds a pragmatic perspective on how researchers and engineers actually work. This presentation will review the details of this methodology and our initial findings.

Two key parameters control the localization of deformation and seismicity along and surrounding crustal faults: the strength and roughness of the preexisting fault surface. Using three-dimensional discrete element method simulations, we investigate how the anisotropy and amplitude of roughness control the mechanical behavior of healed faults within granite blocks during quasi-static triaxial compression. The results show that the localization of fracture development into a damage zone surrounding the initially weak fault zone coincides with the macroscopic failure of the rock. Rougher faults produce more gouge than smoother faults, providing an explanation for the weak influence of roughness on compressive strength. The particles within smoother fault zones slip with higher maximum fault-parallel velocities than rougher faults during the quasi-static loading, likely because the asperities do not impede slip as effectively in the smoother fault zone. The maximum fault-parallel velocity occurs after the peak stress, and falls to a steady state value by the end of the simulation, highlighting the non-constant evolution of slip despite the constant axial strain rate loading conditions. Smoother faults develop stronger correlations between the fault topography and fault slip magnitudes, likely because smoother faults experience higher velocities than rougher faults. Thus, fault surface asperities control slip by acting as speed bumps that hinder fault-plane parallel slip and promote fault-plane normal opening. These numerical models provide insights into the evolution of damage localization, fault roughness, gouge production, asperity abrasion, fault slip and stress concentrations along initially healed faults of varying roughness.

This work investigates small-scale infiltration dynamics in smooth and rough single fractures using a three-dimensional multiphase pairwise-force smoothed particle hydrodynamics (PF-SPH) model. Gravity-driven infiltration instabilities in fractures under unsaturated conditions can significantly influence the arrival time of tracers or contaminants, and the rapid and localized recharge dynamics in fractured-porous aquifer systems. Here, we study the influence of roughness and injection rate on fluid flow modes and flow velocity. Three types of fractures are considered with different degrees of roughness, including a smooth fracture. Both the rough and smooth fractures exhibit flow instabilities, fingering, and intermittent flow regimes for low infiltration rates. In agreement with theoretical predictions, a flat fluid front is achieved when the flux q supplied to a fracture is larger than the gravitationally driven saturated flux (q > kρg/μ cosφ). To characterize the flow instability, we calculate standard deviations of velocity along the fracture width. For the considered infiltration rates, we find that an increase in roughness decreases the flow velocity and increases the standard deviation of velocity. This is caused by a higher likelihood of flow discontinuities in the form of fingering and/or snapping rivulets. To validate our unsaturated flow simulations in fractures, we estimate the scaling of specific discharge with normalized finger velocity and compute the relationship between fingertip length and scaled finger velocity, and find a good agreement with experimental results.

Temporally variable environmental microseismicity and narrowband signals are both demonstrated to reduce the detectability of small seismic events. We investigate the influence of winter wind events on detection thresholds for a 3-sensor seismic network at the terminus of Taylor Glacier, Antarctica. As wind speeds increase, we observe higher spectral amplitudes across the frequency spectrum; however, some frequency bands are preferentially excited. Surprisingly, these spectral peaks shift frequencies through time. To determine detection thresholds, we implement a waveform injection routine wherein we add scaled waveforms to the datastream, and track changes in the size of the smallest scaled event that we can reliably detect. We thereby demonstrate a capability to quantify the size of the smallest detectable event in temporally variable signal environments. Lastly, we propose a method to forecast our ability to detect sources of a threshold size in measured noise conditions.

Heat has historically resulted in more mortality than any other severe weather-related phenomenon in the United States, with the U.S. Centers for Disease Control attributing more than six hundred deaths to extreme heat each year. Agricultural workers are particularly vulnerable to environmental heat exposure: mortality rates due to heat amongst crop workers are twenty times higher than in the general worker population. The vulnerability of this group of workers to heat is exacerbated by factors such as economic vulnerability, immigration status, language barriers, access to health care, and the absence of worker heat standards in all but three of the fifty United States. Climate change threatens to increase the risk of heat-related illness in agricultural workers, but so far there has been no nation-wide evaluation of the expected change in heat exposure in a warmer climate for this group of workers. Here, we estimate baseline and future exposure of agricultural workers in the main growing regions of the continental U.S. to extreme heat. Given the diverse health impacts of different types of heat events, we compare metrics for both single day extremes and multi-day heat waves, and calculate how these statistics will change at the county level with 2 and 4 degrees Celsius of global warming. Our findings serve as a guideline for determining regionally appropriate heat standards, and underscore the need for agencies in all agricultural states to make preparations for a warmer climate.

For more than 20 years, the work of academic climate scientists and those at groups like the Union of Concerned Scientists has been to analyze, synthesize, and convey the projected and, in recent years, current impacts of climate change. In the earlier years, these impacts were still largely imperceptible and pathways for solving the problem seemed wide and flexible. A key objective of climate science communication during those years was to establish the legitimacy of our work, which faced tremendous public and policy maker scrutiny, as a simultaneous barrage of well-coordinated disinformation had led to unusual skepticism of the science. Now we are clearly seeing the impacts of one degree Celsius of warming, the current and projected climate impacts themselves have grown, and the solutions pathways have become narrow and steep. While the climate context has changed for the worse over those 20 years, scientists have largely soldiered on with the same reticent communication. Visible climate change impacts and alarming projections for future change have far outpaced the growth in experts’ ability to reach people and inspire them to act with urgency. At UCS, we have consciously and deliberately begun to change the tenor of our climate communications in response to the increasingly dire results our own analyses have generated and against a backdrop of accumulating and similarly dire science. In our most recent work on extreme heat, particularly in introducing and concluding our results, we have embraced starkly visceral and impassioned language because, as scientists, we see how dangerous the future looks, how serious this moment is in the arc of climate change, and our own moral obligation to communicate it. In this presentation, we will examine the communications space our climate scientists at UCS—as well as those at other institutions—have leaned into. As sentient humans who see an emergency measured not just by data points in charts and graphs but by the faces of those whose homes have flooded, whose loved ones have lost their lives during heat waves, or whose livelihoods are no longer viable, we can’t “tell it like it is” without letting go of reticence and objectivity. The words we choose may be perceived as dangerous, but they are not as dangerous as the world we are ushering in. The house is objectively on fire. It’s time to shout.

Globally available, georeferenced data from earth observing satellites and coupled earth systems models provide new opportunities to use limited field observations to infer trends in environmental processes across unsampled locations and over time. Building statistical models that are intended generalize earth surface processes across large spatial scales represents a new frontier in supervised statistical inference. For example, care must be taken to collect unbiased samples given the complexity of the earth system: surface features can appear vastly different from the perspective of multispectral and SAR imagery in different atmospheric/landscape contexts. Environmental processes occur at variable spatial/temporal scales, and sampling resolution can drastically alter the appearance of patterns, and lead to spatial and temporal autocorrelation which can bias model weights and/or parameter estimates. Multicollinearity in multivariate datasets, which is also scale dependent, can inflate variance in parameter/weight estimates. All these in tandem can undermine the robustness of models in predicting in out-of-sample contexts. To overcome this, the GRRIEn (Generalizable, Reproducible, Robust, and Interpretable Environmental) analysis framework is introduced as a standard method of training and validating supervised data-driven models at large spatial scales. The method is explained, and demonstrated with a case study detecting surface water at CONUS scale using SAR and multispectral imagery.

We are using radio occultation (RO) measurements from Mars Global Surveyor to investigate the nighttime structure and dynamics in the lower atmosphere of Mars. High-resolution temperature profiles retrieved from the RO data contain unique information about nocturnal mixed layers (NMLs) – detached layers of neutral stability that form at night in response to radiative cooling by a water-ice cloud layer. Basic properties of the NMLs and constraints on their spatial distribution and seasonal evolution can be obtained through analysis of the RO profiles. We have examined more than 3000 RO profiles in a latitude band centered on the Phoenix landing site (234°E, 68°N), where nighttime water-ice clouds were observed by the LIDAR instrument (Whiteway et al., Science 325, 68-70, 2009). NMLs appear routinely in the western hemisphere in RO observations at 5 h local time from early summer of MY27. There is a close resemblance in both thickness (a few km) and altitude (about 4 km above the surface) to the cloud layer observed at the same local time by the Phoenix LIDAR in MY29. The NMLs confirm that radiative cooling by the Phoenix cloud is sufficient to trigger convective instability, as predicted by a Large Eddy Simulation (Spiga et al., Nat. Geosci. 10, 652-657, 2017). We have also analyzed more than 800 RO profiles from the northern tropics near summer solstice of MY28. Tropical NMLs are largely confined to regions of elevated terrain, where the daytime convective boundary layer is deep. At 4 h local time, the top of the NML is about 10 km below the peak of Olympus Mons. The spatial distribution of the NMLs appears to be influenced by diverse processes ranging from topographic circulations to planetary-scale thermal tides. In addition, we are using a Mars Global Circulation Model and Large Eddy Simulations to interpret the RO results. Goals of the modeling effort include: to identify the atmospheric processes that control the formation of nocturnal water ice clouds; to understand the spatial distribution of the clouds and their evolution with time of day and season; and to assess the impact of NMLs on the nighttime weather and water transport in the lowest scale height above the surface.

Integrating detrital zircon U/Pb and whole-rock Nd data throughout the Himalayan arc provides the means to distinguish between the Tethyan (TH), Greater (GH), and Lesser Himalayan (LH) tectonostratigraphic zones within the thrust belt. In the Kaghan valley of northern Pakistan, debate exists over the existence and location of the Main Central thrust (MCT) and the tectonostratigraphic affinity of the rocks in the Hazara-Kashmir syntaxis. Three new detrital zircon U/Pb age spectra and two new εNd(0) values from the footwall rocks of the Batal fault yield 1.0-2.0 Ga age populations and an average εNd(0) value of -13.6. Four new detrital zircon U/Pb age spectra and two new εNd(0) values from the hanging wall rocks have primarily <1.2 Ga age populations and an average εNd(0) value of -16.7. Most detrital zircon analyses exhibit Pb-loss from younger (<600 Ma) intrusive or metamorphic events. The absence of zircon <1.0 Ga in the footwall samples indicates that they are LH rocks, while εNd(0) values indicate that they are not Paleoproterozoic LH rocks, but are comparable to other Meso- and Neoproterozoic LH rocks along the Himalayan arc. Neoproterozoic and younger detrital zircon age populations from the hanging wall samples and the presence of ~47 Ma intrusive leucogranite indicate that these are either TH or GH rocks, with εNd(0) values also consistent with TH or GH values. Combining these data show that the hanging wall rocks are either TH or GH rocks, and that the footwall rocks are Mesoproterozoic LH rocks. In most places, the MCT is defined as GH rocks thrust over LH rocks. However, in NW India, the GH/LH contact is buried in the subsurface, and the MCT at the surface is a TH/LH contact. Therefore, these data define the Batal fault in the Kaghan valley as equivalent to the MCT, with TH/GH rocks thrust over LH rocks, and link it with the Indus River valley to the west in Pakistan and NW India to the east.

Amazon Basin deforestation has been driven by agricultural expansion and other developments. Interactions between deforestation, fire, climate, and drought have led to changes in precipitation patterns and river discharge. Previous work suggests that these changes are amplified by land use. Therefore, it is important to understand the degree of change in precipitation patterns and rainy season characteristics. We used long term daily gauge measurements and remote sensing data to analyze the variability and seasonality of rainfall patterns over the Amazon Basin. We focused on the PERSIANN-CDR and CHIRPS precipitation datasets from 1983 to 2018 to quantify trends in predefined indices. The indices that were analyzed to assess variability of precipitation are NDD (Number of dry days); NXE (number of extreme events) during both wet and dry seasons; ORS (Onset of Rainy Season); and ERS (End of Rainy Season). We analyzed the trends for statistical significance and spatial similarity to identify hot spots of change. To connect pattern to process, we also simulated the land-atmosphere system using WRF to assess coupling strength and causality. We are running the simulation using CFSR 2010, with grid resolution of 16 km with the convection scheme active to capture small scale convective rainfall. Previous evidence has suggested an increasing trend on NDD during the dry season, a shift to a later onset and later cessation of the rainy season window, and an increasing trend in the NXE during both wet and dry seasons. The significance and spatial distribution of changes may vary over the region, but we anticipate that in the area with the largest percent of deforestation we will see the highest amount of changes in precipitation.

Slow-slip events were discovered in many subduction zones during the last two decades thanks to recordings of the displacement of Earth’s surface by dense GPS networks. They can last from a few days to several years, and have a relatively short recurrence time (months to years), compared to the recurrence time of regular earthquakes (up to several hundreds of years), allowing scientists to observe and study many complete event cycles. Moreover, whereas regular earthquakes occur along the shallow part of the dipping plate boundary (in the seismogenic / locked zone), slow-slip events often occur on the plate boundary downdip of the locked zone. Slow-slip events could potentially trigger large earthquakes. This phenomenon provides a potential opportunity to further our understanding of subduction zone processes, and evaluate the time-varying seismic hazard. Wavelets methods such as the Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) are mathematical tools for analyzing time series simultaneously in the time and the frequency domain by observing how weighted averages of a time series vary from one averaging period to the next. In this study, we use wavelet methods to analyze GPS recordings of slow-slip events in New Zealand. An important application of the DWT and the MODWT is the estimation of a signal hidden by noise within an observed time series. We used synthetic time series with slow slip events of different durations, to which a Gaussian noise has been added, and denoised the signal using a wavelet-based method, and a low-pass filter. Although the signal was barely visible behind the noise, we could see a unique ramp-like signal in the data denoised with the wavelet-based method, whereas the low-pass filtered signal showed several ramp-like features which were not present in the original synthetic data. Eventually, we aim to be able to detect possible smaller (magnitude 5) slow-slip events that may be currently undetected with standard methods, detect longer (months to years) slow-slip events that are more difficult to detect than slow-slip events with a short duration (days to weeks), and determine the vertical displacement of the ground surface during a slow-slip event.

In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and, top-down processes, directly affecting species at all trophic levels. We applied analytical approaches to better understand future climate change effects on temperature and salinity in Puget Sound. We used empirical downscaling techniques to derive high resolution time series of future sea surface temperature and salinity, based on scenario outputs of two coarse resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, which were created as part of the CMIP6 - Coupled Model Intercomparison Project Phase 6. We calculated long-term averages for historical simulations, calculated anomalies for future years, and applied a delta-downscaling approach to a Regional Ocean Modeling System (ROMS) time series, yielding short (2020–2050) and long-term (2070–2100) forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity: including higher temperatures in the South Basin and higher salinity in the North Basin. Caveats to our methodology include the assumption that variable relationships are static and cannot represent interactions between large scale and local change, but this study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. The climate projections for Puget Sound oceanography will be used to drive the Atlantis ecosystem model for Puget Sound, an end-to-end ecosystem modeling approach that represents all trophic levels and evaluates the species-level impacts of climate change. This project is part of a Washington State Sea Grant funded project, “Evaluating the effects of Southern Resident orcas recovery actions and external threats in the marine ecosystem of Puget Sound.”

An ENSO event can transition from one event to another in complex ways. An El Niño (La Niña) event can be preceded by a La Niña (El Niño) event to become a cyclic ENSO, by a neutral event to become an episodic ENSO, or by another El Niño (La Niña) event to become a multi-year ENSO. We find that the observed El Niño and La Niña exhibit different complexities in their event-to-event transition patterns. The El Niño is dominated in order by episodic, cyclic, and multi-year transitions, but the reversed order is found in the La Niña. This difference in the transition complexity arises from a subtropical Pacific forcing mechanism that triggers ENSO events. Using observational analyses and forced atmospheric model experiments, we show that a preceding ENSO event can activate a subtropical Pacific forcing mechanism to trigger another ENSO event during the following year. These tropical-subtropical Pacific interactions result in a cyclic ENSO transition if the two ENSO events are of opposite signs or a multi-year ENSO transition if they are of the same sign. The preceding ENSO event should excite deep convections in the tropical Pacific in order to activate the subtropical Pacific mechanism. This requirement enables mean temperatures in the cold tongue and warm pool to respectively control how easily the cyclic and multi-year transitions can occur. This mean state control enables the subtropical Pacific forcing mechanism to result in more multi-year transitions for La Niña than El Niño and more episodic transitions for El Niño than La Niña. Furthermore, a future warmer tropical Pacific is projected to decrease the frequency of occurrence of multi-year ENSO transitions but increase the occurrence of cyclic ENSO transitions.. We find that the CMIP5 and CMIP6 models can reproduce the transition complexity for El Niño but not for La Niña. The models tend to produce too many episodic La Niña events and too few multi-year La Niña events. We are able to link the former deficiency to a weaker than observed subtropical Pacific forcing mechanism in the CMIP5/6 models and the latter to a cold bias in mean state SSTs in the equatorial Pacific in the CMIP5/6 models. To achieve better simulations of ENSO transition complexity, further efforts are to improve the model deficiencies in simulating the SP-onset mechanism and mean SSTs in the equatorial Pacific.

Monsoon low pressure systems (LPS) are synoptic scale tropical disturbances that form over the Indian subcontinent along the quasi-stationary trough axis during the monsoon (June to September) period. Around 14 LPS form every year, accounting for around 44% of monsoon precipitation and 78% of extreme precipitation events over the country. Many past studies have investigated the influence of various topographical features on the Indian monsoon. This study investigates the influence of the Himalayan and Tibetan orography (HTO) on various LPS-related characteristics/features (genesis location, number, tracks, and intensity). The NCAR Community Earth System Model (CESM1.2.2) is used to study the influence of HTO on monsoon and LPS activity over India. Simulations from CESM1.2.2 are obtained at 0.9°×1.25° horizontal resolution by considering the present-day height (h) of HTO, and altered heights (zero, 0.5h, and 1.5h). A 9.3% increase in the average monsoon precipitation is simulated over India when the height of HTO is increased to 1.5h, while a decrease in the same by 11.5% (44%) is simulated when the height of HTO is reduced to 0.5h (zero). These results are consistent with previous modeling studies. The changes in monsoon precipitation are attributed to a strong (weak) mean meridional temperature gradient (MTG) associated with an increase (a decrease) in the height of HTO and the prevention of cold dry mid-latitude air mixing with the warm humid air over India. Furthermore, we find that the simulated number of LPS per year increases when the height of HTO is reduced. The number of LPS is 17.2, 16.1, 13.6, and 12.4, respectively, in the simulations where the height of HTO is zero, 0.5h, h, and 1.5h. The mean meridional width of the LPS active region also increases when the height of HTO is reduced (Fig. 1). Contrary to the expectation of a southward shift in the LPS median track with a decrease in MTG (when the height of HTO is reduced), a slight northward shift is simulated in the track’s location. We attribute this to an increase (a decrease) in barotropic instability on reducing (increasing) the height of HTO, which results in a larger latitudinal spread in the location of genesis and tracks. The increased (decreased) barotropic instability also causes an increase (a decrease) in the frequency of LPS over the Indian subcontinent.