Analyses of wildfire-climate relationships over North America were conducted using diverse data including ground-based measurements, satellite retrievals, and re-analyses for the period 1984-2014. Results show the western US (WUS) has experienced the most robust trend in increasing burned area, even though Alaska and central Canada possess comparable or even stronger warming trends compared to WUS. In addition to warming, the WUS has been under the influence of multi-decadal trends in tropospheric relative humidity deficit, reduced cloudiness, increased surface net insolation, enhanced adiabatic warming and drying from increased tropospheric subsidence, as well as drying from enhanced off-shore low-level flow, potentially leading to more abundant dry fuels and raging large wildfires. These trends are likely the manifestation of a regional climate feedback that is enabled by the intensification, and expansion of the North Pacific Subtropical High, associated with a widening of the subsiding branch of the Hadley circulation under greenhouse warming.

Experimental study of the phase and amplitude observations of sub-ionospheric very low and low frequency signals is performed to analyze the response of the lower ionosphere during the August 21, 2017 total solar eclipse in the United States of America. Three subionospheric wave paths have been investigated. The length of the paths varied from 2200 to 6500 km, signal frequencies were 21.4 kHz, 25.2 kHz and 40.8 kHz. Two paths crossed the region of total eclipse and the third path was in the region of 40-60% of obscuration. None of the signals revealed any noticeable amplitude changes during the eclipse while negative phase anomalies (from -35° to -95°) were detected for all three paths. It was shown that the effective reflection height of the ionosphere in low and middle latitudes has been increased by 3.5-5 km during the eclipse.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather stations (PWS) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that PWS data not only improve urban climate models’ evaluation, but can also serve for bias-correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research Forecast (WRF) model and its Building Effect Parameterization with the Building Energy Model (BEP-BEM) activated, we evaluated the modelled temperatures against 407 urban PWS and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using official weather stations only. This finding indicated a need for spatially-explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. Our technique is the first to consider that urban temperatures are heterogeneously accurate in space and that this accuracy is not linearly correlated to the urban fraction. Our results showed that the bias-correction was beneficial to bias-correct daily-minimum, -mean, and -maximum temperatures in the cities. We recommend that urban climate modellers further investigate the use of PWS for model evaluation and derive a framework for bias-correction of urban climate simulations that can serve urban climate impact studies.

Rapid Arctic warming and decline in sea ice have been observed in recent decades. These trends will likely continue, potentially changing winter extremes elsewhere in the Northern Hemisphere. We use coordinated Polar Amplification Model Intercomparison Project (PAMIP) experiments to decompose the Northern Hemisphere winter cold temperature responses to future Arctic sea-ice loss and sea surface temperature (SST) change, separately, at 2C global mean warming. Cold extremes (20-year return period) will generally become warmer at high- and mid-latitudes due to Arctic sea-ice loss, with the largest warming in East Canada. SST change will warm cold extremes everywhere, overwhelming simulated sea ice-induced cooling responses in, e.g., southwestern United States. In general, the SST-induced changes dominate over sea ice-induced changes, with exceptions in East Canada, Nunavut (Canada) and North Pacific Russia. Our results suggest that if climate models do not adequately capture the sea-ice and SST components, cold extremes will be biased.

The intensity and position of the western Pacific subtropical high (WPSH) have crucial effects on climate and disaster events in East Asia during summer. The WPSH significant northward jump (SNJ) events are the main manifestation of the seasonal evolution of WPSH, which are important for the precipitation over East Asia. Using the daily reanalysis datasets from year 1979 to 2020, this study further defines the early and late SNJ events of WPSH on the interannual timescale, which are connected separately with the tropical, mid-latitude subseasonal signals and the local air-sea interaction. However, the mechanisms of the WPSH-SNJ events are different in the anomalous early and late years. In the early SNJ years, the subseasonal signals from the mid-level East Asia-Pacific teleconnection pattern or the low-level boreal summer intraseasonal oscillation cause the positive 500 hPa geopotential height anomalies, which contribute to the significant WPSH northward jump in the first pentad of July. However, the above factors are unable to cause the WPSH-SNJ in the late years. Until the second pentad of August, the collaborative effects between mid-high latitudes wave trains over high levels and cold SST anomalies in the core region lead to the barotropic geopotential height anomalies and the lagged northward jump of WPSH

In June 2021, Canada experienced an intense heat wave with unprecedented temperatures and far-reaching socio-economic consequences. Anomalous rainfall in the West Pacific triggers a cascade of weather events across the North Pacific, which build up a high-amplitude ridge over Canada and ultimately lead to the heat wave. We show that the response of the jet stream to diabatically enhanced ascending motion in extratropical cyclones represents a predictability barrier with regard to the heat wave magnitude. Therefore, probabilistic weather forecasts are only able to predict the extremity of the heat wave once the complex cascade of weather events is captured. Our results highlight the key role of the sequence of individual weather events in limiting the predictability of this extreme event. We therefore conclude that it is not sufficient to consider such rare events in isolation but it is essential to account for the whole cascade over different spatio-temporal scales.

The large-scale electric field in the inner magnetosphere is a key driver of many processes and the dynamics of magnetospheric plasmas. During geomagnetic storms, the enhanced convection electric field is responsible for eroding the plasmasphere and for moving the inner edge of the plasma sheet earthward. In this presentation, we show the preliminary results of an examination of the distribution and variations of the inner magnetospheric quasi-static electric field as measured by the Electric Field and Waves (EFW) instruments onboard the twin spacecraft of the Van Allen Probes mission. We investigate the role that the electric field plays in plasmasphere erosion and plasma sheet access to the inner magnetosphere by analyzing the electric field measurements in conjunction with cold plasma density and plasma sheet particle flux measurements. Since the coupling between plasma populations in the magnetosphere is inherently related to the electric field, we expect that the combined measurements of the electric field and plasmas will enhance our understanding of the physical processes that drive the magnetospheric dynamics.

At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (1) designing satellites; (2) performing adjustments to stay in an intended orbit; and (3) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier-Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents ($N_2$, $O_2$, $O$) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model (GITM). Numerical experiments, Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.

The irregular shapes of atmospheric rivers (ARs) and the scarcity of sounding data have hampered easy AR composite analyses and understandings about AR’s moisture transport mechanism. In this work we develop a method to composite AR-related variables from a reanalysis dataset. By averaging a large number of samples, the three dimensional structure and some evolutionary features of a typical North Pacific AR are revealed. An AR is typically located along and in front of the surface cold front of an extratropical cyclone. A meso-scale secondary circulation is observed in the cross-sections of the AR corridor, where both geostrophic and ageostrophic winds make indispensable contributions to the strong moisture transport. Geostrophic moisture advection across the cold front within the Equatorward half of the AR is created by the baroclinicity of the system, and serves as the primary moisture source of the AR-resided atmosphere. Moisture fluxes from the warm sector of the cyclone are primarily due to ageostrophic winds within the boundary layer, and are more important within the poleward half the AR, particularly during the genesis stage. The faster movement speed of the AR compared with low level winds enables the ARs to collect downwind moisture. While within the Equatorward half moisture transport is mostly attributed to geostrophic advection carried along by the propagating AR-cyclone couple. Driven by the intensifying geostrophic winds, ARs tend to reach peak moisture transport intensity about two days after genesis. Then reduced moisture and influxes from lateral boundaries prevent further moisture flux intensification.

Substantial progress on machine learning (ML) models and graphical processing units (GPUs) has stimulated emerging research in applications of ML to earth science. As snow is a vital component of the global hydroclimate system, precise snowpack prediction is of considerable value for science and society. In this work, we have trained three different ML models (LSTM, CNN and Attention) to predict daily snow water equivalent (SWE) with both dynamic and static features in the Western Contiguous United States from Snow Telemetry (SNOTEL) observations. Dynamic features include precipitation, minimum and maximum temperature, minimum and maximum relative humidity, specific humidity, solar radiation and wind velocity. Static features are latitude, longitude, elevation, diurnal anisotropic heating (DAH) index and topographic radiative aspect (TRASP) index. This choice of features allows us to produce high-resolution maps of regional SWE for a given set of input meteorological conditions. The importance and the sensitivity of input variables will be tested by several explainable AI methods including feature permutation and integrated gradient. The ML-based dataset is further up-sampled and compared with the 4km gridded SWE dataset from the National Snow & Ice Data Center (NSIDC), which is from a physical-based model. Future SWE estimates are also produced under climate conditions projected by CMIP class models, along with associated uncertainty estimates based on our sensitivity analysis. The ML models are demonstrated to be a fast and accurate method of producing high-resolution SWE estimates with minimal computing power.

The term “weather whiplash” was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a frigid cold spell to anomalous warmth or from drought to prolonged precipitation. These events are often highly disruptive to agriculture, ecosystems, and daily activities. In this study we propose and demonstrate a novel metric to identify weather whiplash events (WWEs) and track their frequency over time. We define a WWE as a transition from one persistent large-scale circulation regime to another distinctly different one, as determined using an objective pattern cluster analysis called self-organizing maps (SOMs). We focus on the domain spanning North America and the eastern N. Pacific Ocean. A matrix of representative atmospheric patterns in 500-hPa geopotential height anomalies is created. We analyze the occurrence of WWEs originating with long-duration events (defined as lasting 4 or more days) in each pattern, as well as the associated extremes in temperature and precipitation. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured using internal matrix distances and thresholds. Changes in WWE frequency are assessed objectively based on reanalysis and climate model output, and in the future with climate model projections. Temporal changes in the future under RCP 8.5 forcing are more robust than those during recent decades, with consistent increases (decreases) in WWEs originating in patterns with an anomalously warm (cold) Arctic.

We investigate ionospheric disturbances using the total electron content (TEC) data retrieved by the three satellites after the foreshock of the 2011 Tohoku Earthquake on 9 March 2011. Co-seismic ionospheric disturbances (CIDs) appeared to extend from an onset point concentrically in all of the satellite data. We have found, however, that the geographic coordinates of the onset points did not coincide if the observed CIDs were assumed to occur at one altitude. Admitting that the altitudes of the onset points are different, we searched for coinciding geographic coordinates of the onset points using the two data sets by changing the altitudes and identified the altitude of the two onset points at 155.4 and 234.9 km, and the onset time at these altitudes. As a result, the vertical velocity of acoustic wave is estimated to be 1.04 km/s from the travel time between the altitudes of 155.4 and 234.9 km. This is 1.4 times higher than the sound velocity calculated using the empirical model NRLMSISE-00. The present study provides a method of determining the source location of the acoustic wave from the ionospheric TEC analysis without using the seismic data.

The contribution of individual aerosol species and greenhouse gases to precipitation changes during the South Asian summer monsoon is uncertain. Mechanisms driving responses to anthropogenic forcings needs further characterization. We use an atmosphere-only climate model to simulate the fast response of the summer monsoon to different anthropogenic aerosol types and to anthropogenic greenhouse gases. Without normalization, sulfate is the largest driver of precipitation change between 1850 and 2000, followed by black carbon and greenhouse gases. Normalized by radiative forcing, the most effective driver is black carbon. The precipitation and moisture budget responses to combinations of aerosol species perturbed together scale as a linear superposition of their individual responses. We use both a circulation-based and moisture budget-based argument to identify mechanisms of aerosol and greenhouse gas induced changes to precipitation, and find that in all cases the dynamic contribution is the dominant driver to precipitation change in the monsoon region.

Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into forecasts from the NASA Unified - Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on July 11, 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Assimilation of the observed PBLH was found to improve the temperature, water vapor and velocity profiles relative to independent sonde profiles in the late afternoon, while little improvement was seen during the night and early morning. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements at this time.

Permafrost plays an important role in the hydrology of arctic/subarctic regions. However, permafrost thaw/degradation has been observed over recent decades in the Northern Hemisphere and is projected to accelerate. Hence, understanding the evolution of permafrost areas is urgently needed. Land surface models (LSMs) are well-suited for predicting permafrost dynamics due to their physical basis and large-scale applicability. However, LSM application is challenging because of the large number of model parameters and the complex memory of state variables. Significant interactions among the underlying processes and the paucity of observations of thermal/hydraulic regimes add further difficulty. This study addresses the challenges of LSM application by evaluating the uncertainty due to meteorological forcing, assessing the sensitivity of simulated permafrost dynamics to LSM parameters, and highlighting issues of parameter identifiability. Modelling experiments are implemented using the MESH-CLASS framework. The VARS sensitivity analysis and traditional threshold-based identifiability analysis are used to assess various aspects of permafrost dynamics for three regions within the Mackenzie River Basin. The study shows that the modeller may face significant trade-offs when choosing a forcing dataset as some datasets enable the representation of some aspects of permafrost dynamics, while being inadequate for others. The results also emphasize the high sensitivity of various aspects of permafrost simulation to parameters controlling surface insulation and soil texture; a detailed list of influential parameters is presented. Identifiability analysis reveals that many of the most influential parameters for permafrost simulation are unidentifiable. These conclusions will hopefully inform future efforts in data collection and model parametrization.

Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) conducted with state-of-the-art atmospheric models. To deal with model biases, a modification of the standard 4D-Var algorithm, called weak-constraint 4D-Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the ECMWF operational forecasting system. Despite different origins and applications, Data Assimilation and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from Radio Occultation (RO) measurements. Neural Networks require a large number of samples to properly capture the relationship between the temperature first-guess trajectory and the model bias. As running the IFS data assimilation system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial Neural Networks are trained using the ERA5 reanalysis before using transfer learning on one year of the current IFS model. Preliminary results show that convolutional neural networks are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D-Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.

Greater public, and research, focus on oceanic and atmospheric oxygen budgets is needed. Oceans are “losing their breath”; there has been a 1-2% loss, 77-154 gigatons (GT) of total oceanic oxygen (O2) 7,700GT (est.), over the last 50 years. Low O2 ocean areas occur more frequently and more widely; O2 minimum zone (OMZ) depths are rising; and sulphidic events are more common. Where is lost ocean oxygen ‘gO2ing’? Oxygen (O2) is recharged by phototropic O2 producing organisms in both the oceans and on land. Pre-human activity, O2, and CO2 (190-260ppm.) levels likely oscillated within a relatively stable band, for 800,000 years or more. Thus, O2 usage by volcanism, fires, and soil and sea biomes, must have balanced ocean and land based photosynthetic O2 production, as cyclically regulated by ‘Gaian’ feedback. Therefore, additive anthropogenic related increases in CO2 production, must result in net atmospheric O2 loss, including ocean outgassing. Importantly, exchange of oxygen between the oceans and atmosphere is determined by ‘Henry’s Law’, as influenced by oceanic temperature change (warmer waters dissolve less oxygen), and also salinity. Undissolved oxygen concentrations within upper ocean layers, will be higher due to the water pressure gradient, so less oxygen being dissolved at lower pressures. Thus, subject to usage by sea life forms; and thermoclines and haloclines; will likely be replenished from below, tending to surface saturation, even if the OMZs are rising so decreasing in depth? Henry’s law dictates, as anthropogenic reduction of atmospheric O2 reduces partial pressure, dissolved ocean oxygen will equilibrate, releasing stored oxygen to the atmosphere. Thus, over time, if atmospheric O2 continues to fall due to anthropogenic usages, irrespective of other factors, oceanic O2 will arguably be diminished year on year; ultimately will oceans become sufficiently anoxic to be unstable? Research suggests risk of species extinction type events of varying severity strongly correlate to anoxic ocean events. Due to Henry’s Law; will continued anthropogenic oxygen including fossil fuel use, inevitably lead to oceanic oxygen outgassing depletion, and absent change, ultimately an extinction event risk? Is a global Manhattan type project needed to find viable non-carbon-based, non-oxygen using, energy sources?

The paper inspects the effects of a G2 class geomagnetic storm that occurred during 27 February- 1 March, 2014 on the equatorial ionization. This storm is observed following a Coronal Mass Ejection (CME) from a sunspot AR1967 on 26 February. An enhancement of solar wind speed is observed on 27 February, 2014 (483 km/sec). The maximum southward component of Interplanetary Magnetic Field (IMF) is observed around 21 UT of 27 February (12 nT). This interconnects with Earth’s magnetic field and develops the main phase of a geomagnetic storm on the same day. The storm continues through 28 February and quiet-time ionospheric condition is recovered on 1 March. The effects of the storm on equatorial ionization is observed at Brasilia (15.95°S, 47.88°W geographic; 9.40°N, 21.13°E geomagnetic), Addis Ababa (9.04°N, 38.77°E geographic; 0.18°N, 110.47°E geomagnetic) and Colombo (6.89°N, 79.87°E geographic; 1.57°S, 151.57°E geomagnetic). An enhancement of TEC is observed during main phase of the geomagnetic storm at these stations. Increment in diurnal peak is observed on 28 February (14 TECU at 10 UT at Addis Ababa) while a decrement of diurnal peak TEC is observed during the recovery phase of the storm (15 TECU at 10 UT at Addis Ababa). Post-sunset ionospheric scintillation is inhibited at Brasilia on 28 February.

The unambiguous 2-dimensional (2D) maps of OI 135.6 nm radiance retrieved from the Global Observation of Limb and Disk (GOLD) after sunset are compared with the total electron content (TEC) maps measured by GPS receivers in the America sector. The OI 135.6 nm radiance observed by GOLD is an indicator of the peak electron density of the ionosphere, while the TEC depends on the total electron density in the column. The comparisons show that both of them are able to capture the large structures in the equatorial ionization anomaly (EIA) well, and sometimes they both also capture bubbles. Both show that the ionosphere after sunset is quite dynamic and has strong day-to-day variability. A statistical study has also been carried out to check the occurrence rate of bubbles and the apparent EIA structure between Oct 17, 2018 to May 31, 2019. GOLD is able to image the areas where it is difficult to situate GPS receivers such as the ocean, while TEC data covers the full-diurnal cycle. In all, the GOLD and TEC have valuable synergy to allow us to gain a better understanding of the equatorial ionosphere.

Mixed-phase clouds are ubiquitous in the Arctic and play a critical role in Earth’s energy budget at the surface and top of the atmosphere. These clouds typically occupy the lower and midlevel troposphere and are composed of purely supercooled liquid droplets or mixtures of supercooled liquid water droplets and ice crystals. Here, we review progress in our understanding of the factors that control the formation and dissipation of Arctic mixed-phase clouds, including the thermodynamic structure of the lower troposphere, warm and moist air intrusions into the Arctic, large-scale subsidence and aerosol particles. We then provide a brief survey of numerous Arctic field campaigns that targeted local cloud-controlling factors and follow this with specific examples of how the Arctic Cloud Observations Using airborne measurements during polar Day (ACLOUD)/ Physical feedback of Arctic PBL, Sea ice, Cloud And AerosoL (PASCAL) and Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX) field campaigns that took place in the vicinity of Svalbard in 2019 were able to advance our understanding on this topic to demonstrate the value of field campaigns. Finally, we conclude with a discussion of the outlook of future research in the study of Arctic cloud-controlling factors and provide several recommendations for the observational and modelling community to advance our understanding of the role of Arctic mixed-phase clouds in a rapidly changing climate.

Sanbon Chaka Gosa1, Bogale Abebe Gebeyo12, Ravitejas Patil1, Ramón Mencia1, Dani Zamir1, Menachem Moshelion1# 1 The R.H. Smith Institute of Plant Sciences and Genetics in Agriculture, The R.H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, 76100 Israel 2 Current address: Department of Horticulture, College of Agriculture and Natural Resource, Dilla University, Dilla, Ethiopia #Corresponding Author Abstract Plant productivity in general and under stress, in particular, is a complex and comlitative trait largely influenced by environmental conditions. Among the most important traits are the whole-plant water-balance regulation mechanisms that dynamically change in order to maximize the metabolic activity of the plant. Due to the difficulty of high-throughput phenotyping of these physiological traits (e.g. transpiration, stomatal conductance, and photosynthesis), they are usually measured in static conditions or modeled based on only a few measuring points (low resolution). To overcome this challenge, we utilized a high-throughput gravimetric functional-phenotyping platform (PlantArray) along with a practical reverse phenotyping approach. We selected 30 tomato lines from multiple years of field yield data and functionally phenotyped them for their dynamic response curves using a variety of stress scenarios implemented using drought conditions (each plant received irrigation based on the amount of water it transpired). Our results show that resilient and tolerable traits, in the field, are associated with stomatal plastic conductance, i.e., maximum under well-irrigation, yet the rapid response to changes in environmental conditions (soil and atmospheric). The plastic traits of the idiotype lines were shown to increase water use efficiency (momentarily), thus maximizing yield in water deficit conditions. Based on manual characterizations of the idiotypes, it has been found that their abaxial surfaces have a greater density of stomata and a higher aperture during the early morning. Additionally, these lines showed rapid recovery after a drought. Our study concluded that reverse functional phenotyping can significantly reduce the pre-breading processes for yield-related traits. Keywords: Functional phenotyping, crops yield, —dynamic response, drought stress, stomatal conductance, reverse phenomics

Volcanic flood basalt eruptions have been linked to or are contemporaneous with major climate disruptions, ocean anoxic events, and mass extinctions throughout at least the last 400M years of Earth’s history. Previous studies and recent history have shown that volcanically-driven climate cooling can occur through reflection of sunlight by H2SO4 aerosols, while longer-term climate warming can occur via CO2 emissions. We use the Goddard Earth Observing System Chemistry-Climate Model to simulate a four-year duration volcanic SO2 emission of the scale of the Wapshilla Ridge member of the Columbia River Basalt eruption. Brief cooling from H2SO4 aerosols is outweighed by dynamically and radiatively driven warming of the climate through a three orders of magnitude increase in stratospheric H2O vapor.

This poster presents field-scale numerical compositional simulations of CO2 storage mechanisms in the Morrow B sandstone of the Farnsworth Unit (FWU) located in Ochiltree County, Texas. The study examines structural-stratigraphic, residual, solubility and mineral trapping mechanisms. The reactive transport modeling incorporated evaluates the field’s potential for long-term CO2 sequestration and predicts the CO2 injection effects on the pore fluid composition, mineralogy, porosity and permeability. The dynamic CO2 sequestration simulation model was built from an upscaled geocellar model for the Morrow B. This model incorporated geological, geophysical, and engineering data including well logs, core, 3D surface seismic and fluid analysis. We calibrated the model with historical CO2-WAG miscible flood data and used it to evaluate the feasibility and mechanisms for CO2 sequestration. We used the maximum residual phase saturations to estimate the effect of gas trapped due to hysteresis. In addition, gas solubility in the aqueous phase was modelled as function of pressure, temperature and salinity. Lastly, the coupled geochemical reactions, i.e., the characteristic intra-aqueous and mineral dissolution/precipitation reactions were assimilated numerically as chemical equilibrium and rate-dependent reactions respectively. Additional scenarios that involve shut-in of wells were performed and the reservoir monitored for over 1000 years to understand possible mineralization. Changes in permeability as a function of changes in porosity caused by mineral precipitation/dissolution were calibrated to the laboratory chemo-mechanical responses. The study validates the effects of Morrow B petrophysical properties on CO2 storage potential within the FWU. Study results shows: EOR at the tertiary stage of field operations, total amount of CO2 stored in aqueous-gaseous-mineral phases, evolution and dissolution/precipitation of the principal accessory minerals and the time scale over which mineral sequestration took place in the FWU. This study relates the important physics and mechanisms for CO2 storage in the FWU and illustrates the use of the coupled reactive flow. The study serves as a is benchmark for future field-scale reactive transport CO2-EOR projects in similar fields throughout the world.

Farmers in the indigenous Andean community of Cañar, Ecuador rely on an abundant wet season each growing year and are concerned that their wet seasons are becoming less rainy. The normal seasonal rainfall pattern in this region, the Andes of southern Ecuador, is driven primarily by the seasonal migration of the Intertropical Convergence Zone (ITCZ). Existing scientific literature suggests that the ITCZ’s seasonal migration pattern could be affected by anthropogenic climate change, thereby impacting the seasonal precipitation in the region. This study explores the possibility that the ITCZ’s migration pattern has already changed, which would validate the concerns of the Cañari people. This is accomplished by tracking the movement of the ITCZ over the past few decades using Outgoing Longwave Radiation (OLR) data. No obvious trend in the seasonal mean position of the ITCZ has been detected. However, OLR maps indicate an abnormal ITCZ signature during strong warm phases of the El Niño-Southern Oscillation (ENSO), consistent with previous studies. The interannual variability in the region’s seasonal precipitation is also associated with varying phases of ENSO. An additional component of this study is the development of a statistical model using ENSO indices as input, among other atmospheric indices, to provide seasonal precipitation forecasts for the tropical Andean wet season each year. Besides ENSO, another potentially useful index for seasonal prediction is the phase of the Quasi-Biennial Oscillation, which has been linked to precipitation variability in the tropics. The seasonal forecasting model is expected to help Ecuadorian highland farmers decide which of their crops to plant each year, based on the water needs of each crop.