The use of an objective seasonal forecast procedure, defined as a traceable, reproducible, and well-documented set of steps that allows the quantification of forecast quality, are preferred and recommended by the World Meteorological Organization in their recent seasonal forecast guidance. However, National Meteorological Services (NMS) in African countries have been issuing seasonal rainfall forecasts using a subjective consensus process, which is based on meteorologists’ experiences using Global Producing Center’s (GPCs) outputs and other available information. A systematic general objective approach named as NextGen (Next Generation) forecasting system is being developed for some East African countries as part of implementing or strengthening ENACTS (Enhancing National Climate Services; https://iri.columbia.edu/resources/enacts/) initiative as well implementing Columbia University’s World Project “Adapting Agriculture to Climate Today, for Tomorrow” (ACToday; https://iri.columbia.edu/actoday/) project. This new forecast system is based on a calibrated multi-model ensemble (CMME) process using state-of-the-art general circulation models (GCM) from the North American Multi-Model Ensemble project. A canonical-correlation-analysis-based regression is used to calibrate the raw outputs from the GCMs; then the individually-calibrated GCMs are combined with equal weight to make a final CMME prediction. In addition to traditional tercile probability forecasts, NextGen also provides a more flexible format that enables users to extract information for those parts of the forecast distribution of the greatest interest to them in the decision-making process. Therefore, NextGen enables NMS to generate and deliver targeted climate information products relevant to the needs of decision-makers at multiple levels. The NextGen forecast system has so far been implemented in Ethiopia, Rwanda, Zambia, Malawi and Tanzania, and planned to be implemented in more countries in the near future. In this study, we describe the co-design, co-development, and skill assessment of this NextGen system.

Pore connectivity, a topological characteristic of pore structure, is oftentimes more important than the geometrical aspects in controlling fluid flow and mass transport in porous natural rocks as well as their associated utilities in energy and environmental stewardship. A different extent of pore connectivity can be reflected in the proportion of isolated pore space not connected to the surface of natural rocks. This work presents the multi-approach and multi-scale laboratory studies to investigating the proportion of isolated pore space of, and its resultant anomalous fluid flow and radionuclide movement in, generic geological barrier materials (clay sediment, crystalline rock, salt rock, shale, tuff). The samples include clay sediments of Wakkanai formation at Horonobe underground research center in Hokkaido of Japan, Opalinus clay of Mt. Terri Underground Research Laboratory as well as granodiorite from the Grimsel Test Site in Switzerland, salt rock from Waste Isolation Pilot Plant in New Mexico, various shales (Barnett, Eagle Ford and Wolfcamp from Texas), and welded tuff in Yucca Mountain in Nevada. Working with sample sizes from <75 μm to several centimeters, the experimental approaches include the independent quantification of both (1) surface-accessible pore space with various probing fluids (e.g., helium in expansion, water in vacuum saturation and nuclear magnetic resonance, mercury in intrusion porosimetry, nitrogen in gas physisorption, and Wood’s metal in high-pressure impregnation and micron-scale tracer mapping using laser ablation-ICP-MS); and (2) total (both connected and isolated) porosity by small angle X-ray scattering. In summary, our evolving complementary approaches provide a rich toolbox for tackling the pore structure characteristics in geological barrier materials, and associated fluid flow & radionuclide transport, implicated in their long-term performance in natural and engineered systems of a nuclear waste repository.

The Global Warming Potential (GWP) is a widely used single-number metric used to compare the climate change effects of various greenhouse gases. Although GWP has an established role in international climate agreements, it is unphysical, unintuitive, arbitrary, ignores the time dependence of emission sources, and is in some cases misleading. The same doubts have been expressed by the convening lead author of the relevant chapter in the First Assessment Report of the Intergovernmental Panel on Climate Change, in which GWP was introduced. GWP and the related CO2-equivalent methodology have no place in describing the effects of climate change mitigation strategies beyond a 20 year horizon. An important element of sound policy making is the quantitative analysis of policy interventions. This work argues for the broader use of temperature change trajectories in educating policymakers and the public about greenhouse gas control. Modeling tools include general circulation models, related reduced complexity models, and simpler models of global mean temperature change. Examples are presented illustrating the misleading nature of GWP and the CO2-equivalent methodology. These include the greenhouse gas impacts of (1) single year and multiyear emissions of methane, (2) natural gas and coal use in the electric power sector, (3) pipeline and LNG transport of natural gas, (4) natural gas flaring, and (5) livestock herd reduction. Responding to climate change is likely to require global investments measured in trillions of dollars. Clear and accurate communication among and between scientists, policy makers. and the general public is essential. GWP is a crude, inaccurate tool that and the general public is essential. GWP is a crude, inaccurate tool that played a constructive role in the formulation of international agreements. Using better modeling tools can help make climate policy discussions more scientifically rigorous while demystifying the criteria upon which policy choices are made.

To support the development of the Global Forecast System (GFS) physics suite and identify opportunities for improving the model physics in the UFS, the Developmental Testbed Center (DTC) conducted an array of analyses for evaluating the operational GFSv15 forecasts and the experimental forecasts using the GFSv16beta physics suite distributed with the UFS Medium-Range Weather Application v1.0 public release. Five-day GFSv16beta forecasts for one boreal winter season were generated using the operational GFS analyses as initial conditions. The evaluation metrics included tools from Model Evaluation Tools (MET) and in-house process-oriented diagnostics. The evaluations focused on the perpetuating GFS forecast errors pertaining to the planetary boundary layer (PBL), land-surface, cumulus, radiation, and cloud processes. The runs using GFSv16beta outperformed the operational GFSv15 with respect to the root-mean-square errors of large-scale environmental variables and the anomaly correlation coefficient for 500 hPa geopotential height. Nevertheless, larger biases associated with key physical processes were identified in the GFSv16beta forecasts. For example, the global precipitation forecast skill degrades and a dry bias remains in the tropics, suggesting a persistent problem in the cumulus scheme. The near-surface and boundary-layer cold biases are larger over most continents and polar regions, which is partly related to the systematic negative temperature errors in the GFS analysis. The overestimated near-surface wind speed particularly at night in the northeastern U.S. implies that the surface drag may be underrepresented. Excessive short-wave radiation reaching the ground in the high-latitudes of the summer hemisphere appears to be related to low cloud liquid and ice water path. These and other results will be described in this presentation.

A gusty wind by typhoons is one of the major natural hazards and has been the most threatening windstorm in urban districts. In recent year some major cities in Japan have experienced extreme winds during typhoon landfalls. For example, Typhoon Jebi (2018) caused extreme wind gusts in Osaka and Kyoto, while Typhoon Faxai (2019) and Hagibis (2019) produced high winds in Tokyo and neighboring cities. Urban roughness obstacles exert significant influences on the magnitude of wind gustiness. With the growing urbanization globally, the quantification of turbulent winds in densely built, urban districts is important to the assessment and prediction of risks of wind damages and the understanding of the underlying physical mechanisms. Influences of densely built urban environments on the occurrence of wind gusts in urban districts during the typhoon landfalls are studied by merging mesoscale meteorological and building-resolving large-eddy simulations (LES), which allows an explicit representation of the complicated building structures while retaining the strong mesoscale perturbations from the typhoon. The actual building data of Osaka, Kyoto, and Tokyo are used in the building-resolving LES computational domains. With the successful reproduction of the track and intensity of the typhoon in meteorological simulations, the simulated winds at the simulated boundary-layer top are used to quantify the wind gusts in the urban district. The maximum wind gust in the analysis area of Osaka during the landfall of Typhoon Jebi is found to exceed 60 m/s, which is comparable to the wind speed at the height of about 300 m. Such wind gusts are generated by the instantaneous downward momentum transfer in areas, where buildings of great height variability are clustered. The instantaneous wind gusts are found to be the strongest for moderate building packing density. The results suggest that the risks of wind damages are mostly likely to be maximized in urban districts of high building-height variability and moderate packing density.

Lunar mare basalts are the products of their corresponding parent magma compositions, sourced from the lunar upper mantle. The lunar mantle has been repeatedly modeled through numeric simulations to reflect lunar magma ocean (LMO) crystallization, resulting in an early-stage anorthositic crust and immediately underlying, late-stage, KREEP-rich and ilmenite-rich layer. This negatively buoyant layer is expected to have induced mixing with the underlying mantle, potentially to the core-mantle boundary. The lunar mare basalts, in this context, reflect mantle sources that are variably mixed between pristine mantle compositions and the dense ilmenite-rich layer. In order to constrain the geometry of lunar mantle heterogeneity, we simultaneously examined multiple mare basalt characteristics to extract significant multivariate patterns that might lend insight into the nature of this mixing-induced heterogeneity. Using two fundamental machine learning approaches and a newly compiled database of Apollo basalt characteristics (ApolloBasaltDB), we conducted a preliminary investigation, holding the assumptions that 1) mare basalts are assumed to retain the majority of their original characteristics at the time of extrusion: texture, isotopic age, major element composition, mineral mode, and geographic occurrence; 2) negligible basalt alteration occurred due to the lack of an atmosphere; and 3) impact gardening did not have significant bearing on final geographic location of basalt samples based on our nearside spatial partitions. The results of cluster and principal component analyses over changing spatial basalt groupings suggest that lunar nearside changes in major element concentrations and mineral modes vary spatially. Al2O3 concentrations increase in diversity within the Procellarum KREEP Terrane (PKT) compared to older regions immediately exterior to the eastern PKT, while a general nearside trend appears to suggest that ilmenite (TiO2) diversity comes at the expense of plagioclase (Al2O3) diversity. Cluster analysis suggests PKT perimeter rifting may have tapped into increasingly Ti-rich sources as rifting proceeded SE to NW. By establishing such trends over varying spatial scales through multivariate processing, the changing strengths of these surface correlative patterns may indicate the changing conditions (including temporal) of the immediately underlying mantle at the time of extrusion.

Precipitation flag (precipitating or not; stratiform or convective) is a key parameter for us to make betterretrieval of precipitation characteristics as well as to understand the cloud-precipitation physicalprocesses. The Global Precipitation Measurement (GPM) Core Observatory’s Microwave Imager (GMI)and Dual-Frequency Precipitation Radar (DPR) together provide ample information on globalprecipitation characteristics. As an active sensor in particular, DPR provides an accurate precipitationflag assignment, while passive sensors like GMI were traditionally believed not to be able to tell apartprecipitation types. Using collocated precipitation flag assignment from DPR as the “truth”, this project employs machinelearning models to train and test the predictability and accuracy of using passive GMI-only observationstogether with ancillary atmosphere information from reanalysis. Precipitation types are classified intothe following classes: convective, stratiform, convective-stratiform mixed, no precipitation, and otherprecipitation. Sub-sampling with different probabilities is employed to construct a balanced trainingdataset. A variety of classification algorithms are tested, including Support Vector Machines, NaiveBayes, Random Forests, Gradient Boosting, and Neural Networks (Multilayer Perceptron Network), andtheir results are evaluated and compared. The trained model has ~ 85% of prediction accuracy for everytype of precipitation. High-frequency channels (166 GHz and 183 GHz channels) and 166 GHzpolarization difference are found among the most important factors that contribute to the modelperformance, which shed light on future instrument channel selection.

Aerosol-cloud interactions are both uncertain and important in global and regional climate models, and especially in the southeast Atlantic Ocean. This uncertainty in the region is largely due to two correlated factors---the expansive, bright, semi-permanent stratocumulus cloud deck and the fact that southern Africa is the largest source of biomass-burning aerosols in the world. We study this region using the WRF-Chem model with CAM5 aerosols and in situ observations from the ORACLES and LASIC field campaigns in August-October of 2016 through 2018. We compare aerosol and cloud properties to measure and improve model performance and expand upon observational findings of aerosol-cloud effects. Relevant comparison variables include aerosol number concentration, mean particle diameter and spread, CCN activation tendency, hygroscopicity, and cloud droplet number concentrations. Specifically, our approach is to analyze colocated model data along flight tracks to resolve aerosol-cloud interactions. Within and between single-day flights, there is high spatiotemporal variability that can get lost to large-scale averaging analyses. We have found that CCN is substantially under-represented in the model compared to observations. For a given aerosol number concentration, size, supersaturation and hygroscopicity, the model will consider fewer particles as CCN than observations indicate. We plan to explore this result further, diagnosing the model-observation differences more consistently and updating the model with more physically accurate values of aerosol size, concentration, or hygroscopicity based on observations. We will also intercompare multiple instrument platforms involved with the ORACLES and LASIC campaigns. With improved small-scale aerosol-cloud interactions, this work also shows promise to substantially improve that representation in climate models.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as “Doppler Flash”, is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first, enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in refractive index in the D and lower E regions due to plasma density enhancement are the primary cause of Doppler Flash.

The new dual-frequency radar satellite ASAR-ISRO provide simultaneously and for the first time a wide wavelength spectrum which is critical to discriminate surface roughness based on different backscattering characteristics. Here we use such dual L-band and S-band airborne SAR system to characterize and map various volcanic areas in the Northern Cascades through their backscattering properties. Mapping volcanic flows (lava flows, pyroclastic currents, lahars) is vastly improved by using backscattering as a metric of surface roughness. Various types of volcanic flow deposits and surface textures are distinguished by their roughness measured with radar systems. The ability of radar systems to distinguish volcanic flow textures, represented by roughness, is a key factor in understanding the processes and timescales of flow emplacement. For instance, transition from pahoehoe to aa lava flows is associated with change in flux and steepness of topographic gradient. Therefore, lava textures are essential data for calibrating and improving lava flow emplacement codes. Similarly, the textures of high velocity, and more deadly, pyroclastic currents and lahars change along their flow paths, also revealing critical data about the mechanisms of flow emplacement. As with lava flows, we use the ASAR-ISRO L+S SAR system to characterize the run-out distances of pyroclastic currents and lahars where large numbers of blocks accumulate in such deposits (places where flow momentum was lost, whether due to friction or break-in-slope and vast quantities of blocks accumulate in a relatively small area compared to the total area inundated by the flow). Mapping volcanic flow textures in a variety of volcanic terrains will provide clues about modes and rates of emplacement, and change in these through time, in a way that is simply unavailable by traditional geologic mapping. The next generation of volcanic flow maps, used for hazard assessment, will rely on radar data to delineate these textures.

My earth science focused PhD research included the analysis of annual fluorescent laminae in cave stalagmites, under the supervision of karst hydrologist Peter Smart (Bristol, UK). At the time, the source of this fluorescence was uncertain, opening new research opportunities characterizing what is now understood to be soil-derived, water-soluble fluorescent dissolved and colloidal organic matter that is transported to the cave by vadose zone percolation waters. Aquatic organic matter fluorescence research benefitted at this time from significant laboratory analytical advances that were commercially driven, including Sony’s Blu-ray technology and the use of fluorescent labelling in the biomedical sciences. Faster analyses at increasingly higher energy excitation energies opened opportunities for novel fingerprinting of organic matter in hydrological science, including landfill leachates and sewage contamination. Today, hand-held fluorescence sensors can instantaneously determine microbial water quality. And back in the field of speleothem (cave deposit) science, annual geochemical laminae are now recognized to be widely preserved in regions where there is a seasonality in recharge, providing a precise chronology for the stalagmite paleoenvironmental archive. Currently, we are utilizing this precise chronology to generate high-resolution fire history records, where the fire proxy is water-soluble ash-derived elements transported from soil to cave by vadose zone percolation waters.

We utilize the Planet Model Code [1] to model the probability of survival of Earth-like life on habitable worlds (to 3 billion years post-abiogenesis). The Planet Model Code [1] was originally created by Tyrrell [2] to investigate the chances of intelligent life. This necessitates stability in a rather narrow temperature range. Here we expand the focus to the survival of any life, including extremophiles, with greater tolerance for variations in temperature. Specifically, we investigate the relationship between the long-term survival probability of life and larger temperature variations. Keywords: Exoplanet; Habitable; Prokaryote; Matlab; Planet Model Code References: 1. T. Tyrrell, Planets model code, (Oct. 2020) https://doi.org/10.5281/zenodo.4081451. 2. T. Tyrrell, “Chance played a role in determining whether earth stayed habitable”, Commu- nications Earth & Environment 1, 1–10 (2020).

Oceanic lithosphere created at mid-ocean ridges (MOR) is shaped by a complex interplay of magmatic, tectonic and hydrothermal processes. Where melt budget decreases at (ultra)slow spreading rates, the tectonic mode changes from normal to detachment faulting. We investigate an endmember type of detachment faulting, the so-called “flip-flop” detachment mode, observed exclusively at almost amagmatic sections of ultraslow-spreading MORs (e.g. Southwest Indian Ridge 62°E to 65°E): Active detachments migrate across the ridge axis towards the hanging wall side until they are superseded by a new on-axis fault of opposite polarity. Using a steady-state temperature field, numerical experiments by Bickert et al. (EPSL, 2020, 10.1016/j.epsl.2019.116048) show that an axial temperature maximum is essential to trigger flip-flop faults by focusing flexural strain in the footwall of the active fault. However, ridge segments without a significant melt budget are more likely to be in a transient thermal state controlled, at least in part, by the faulting dynamics themselves. In our study we investigate (1) which processes have first order control on the thermal structure of the lithosphere, (2) their respective feedbacks on the mechanical evolution of the lithosphere, and (3) how they facilitate flip-flop detachment faulting. We present results of 2-D thermo-mechanical numerical modelling including serpentinization reactions and dynamic grain size evolution. The model features a novel form of parametrized hydrothermal cooling along fault zones as well as the thermal and rheological effects of periodic sill intrusions. We find that both hydrothermal cooling of the active fault zone and periodic sill intrusions in the footwall are essential for entering the flip-flop detachment mode. Hydrothermal cooling of the fault zone pushes the temperature maximum into the footwall, while intrusions near the temperature maximum further weaken the rock and facilitate the opening of new faults with opposite polarity. We conclude that this interplay of hydrothermal cooling and magmatic intrusions is required to produce the observed flip-flop faulting. Our model allows us to put constraints on the magnitude of both processes, and we obtain reasonable melt budgets and hydrothermal heat fluxes only if both are considered.

Origins of the interdecadal fluctuations of the ENSO-Indian summer monsoon (ISM) teleconnection strength measured by running window correlation coefficients have been much debated. The main question is whether it’s due mostly (i) to sampling errors, or, (ii) under internal variability decadal time scale drivers modulate it, and/or, (iii) it has undergone some forced change recently. A new statistical test does not detect any of (ii-iii) in observations. However, it does not mean that there could not be such effects, just that the data is insufficient. Large ensemble data sets are well suited to investigate these questions. Previously, (iii) was found to be small (even if possibly important) in the MPI-GE data set, and the situation is similar in the new CESM2-LE data set. This time we investigate (ii), too, and find that the decadal variability of e.g. the Dipole Mode Index (DMI*, decorrelated from Nino3) of the Indian Ocean “explains” 4% of the apparent variability of the ENSO-ISM teleconnection. If the CESM2-LE were faithful to reality, then this signal would take two millenia to have a 50% chance to detect. We also find that this apparent DMI* influence is due to heteroscedasticity, namely, to the apparent DMI* influence on ENSO variance. However, we also find that this influence is mostly apparent, indeed, as it is the ENSO variance that dominantly influences the DMI*.

Globally consistent natural evidence on past climate evolution is indispensable for climate model evaluations and forecasts. However, it has rarely been investigated quantitatively whether large sets of globally distributed pollen records with limited dating resolution can be statistically linked. This could facilitate the identification of global in contrast to regional climate change signals on millennial to orbital time scales. We consider a global set of time-irregular pollen records for a joint analysis of spatial similarity on different time scales during the last glacial. Making use of measures suitable for irregular time series and by application of a spatio-temporal stochastic model, we examine significant commonality between pollen records. We quantitatively assess the resulting paleo-climate networks while respecting the spatially heterogeneous and sparse proxy archive layout. The network configurations are compared to synthetic proxy networks, which mimic different real-world record impairments. We find strong commonalities of well resolved Chilean, North Pacific and European records on orbital to millennial time scales. They reveal partly inverted deglaciation signals for westward exposed coastal tree vegetation. Such signals are consistently observable for several mid-latitude records, probably indicating equatorward shifts of westerly circulation structures during the last glacial. Surrogate data suggests that a notable part of total records might be insufficiently resolved to detect statistically significant record similarity at least when classical correlation-based measures are utilised. We compare the results to temperature and precipitation signals in PMIP3 models.

In this study, the changes in the occurrence of marine fog over the summer North Pacific in warmer sea surface temperature (SST) or increased CO2 climates were investigated based on atmospheric model simulations by using the fifth phase of the Climate Model Intercomparison Project (CMIP5) multimodel data. Initially, the marine fog representation in CMIP5 multimodels was briefly evaluated globally. We found that the simulated marine fog occurrence was represented relatively well in boreal summer but poorly in other seasons. The results indicated that the changes in the North Pacific high‐pressure system accompanied by changes in horizontal wind patterns control the changes in marine fog occurrence in the North Pacific. The magnitude of contrasting pair changes in marine fog occurrence in the western and eastern North Pacific are primarily determined by the magnitude of changes in the North Pacific high‐pressure system. Global‐scale changes in the vertical profiles of the atmosphere (stability changes) can also affect the marine fog changes. These changes in marine fog over the North Pacific were consistent among most CMIP5 models.

The frequency and intensity of new particle formation (NPF) over remote forest regions in the temperate and boreal zones, and thus the importance of NPF for the aerosol budget and life cycle in the pristine atmosphere, remains controversial. Whereas NPF has been shown to occur relatively frequently at several sites in Scandinavia [1], it was found to be nearly absent at a mid-continental site in Siberia [2]. To explore this issue further, we made measurements of aerosol size distributions between 10 and 300 nm diameter at two remote sites in the transition region between temperate and boreal forest in British Columbia, Canada. The measurements covered 23 days during the month of June 2019, at the time when NPF typically reaches its seasonal maximum in remote mid-latitude regions. These are the first such measurements on the North American continent. Although the sites were only 150 km apart, there were dramatic differences in NPF frequency and intensity between them. At the Eagle Lake site, NPF occurred almost daily and nucleation mode particle concentrations reached above 5000 cm-3. In contrast, at the Nazko River site, there were only 6 NPF events in 11 days and nucleation mode particle concentrations reached only about 800 cm-3. The reasons for this difference are under investigation. They may include airmass origins, pre-existing aerosols, density of forest cover in the surrounding regions, and proximity to the Pacific coast. Our results suggest that measurement campaigns in the remote forest regions of North America to investigate the role of NPF with a more comprehensive set of instrumentation are essential for a deeper scientific understanding of this important process. [1] Nieminen et al., Atmos. Chem. Phys. 18 (2018) 14737-14756. [2] Wiedensohler et al., Atmospheric Environment, 200 (2019) 167-169.

Autonomous large-scale detection of whistler-ode chorus elements in the Van Allen radiation belts has been an open computational challenge. This is primarily due to: (i) Variability of the spectral morphology of chorus elements, and (ii) Structured background interference from hiss-like chorus that can make elements difficult to detect using traditional signal processing and pattern recognition techniques. We will present computational techniques drawing on pixel connectivity, signal-to-noise (SNR) considerations as well as supervised and unsupervised pattern recognition techniques. Specifically, we will explore the efficacy of popular machine learning techniques trained on unfiltered spectral images versus those trained on culled features generated by unsupervised feature extraction techniques. Representative results will be presented based on magnetic field measurements taken by the EMFISIS instrument suite in the Van Allen probes mission.