Weather generators based on resampling simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are coherent with the observed ones. These weather generators are fully data-driven and simple to implement, do not rely on parametric distributions, and can reproduce the dynamics among the weather variables under analysis. However, although the simulated time series is new, the produced weather fields at arbitrary timesteps are copies of the weather fields found in the training dataset. Consequently, the spatial variability of simulations is restricted. Furthermore, these weather generators cannot create weather fields with out-of-sample extreme values because the scope of the resampling method is constrained to the observed values. In this work, we embedd the Direct Sampling algorithm — a data-driven method for producing simulations — into resampling-based weather generators to improve the spatial variability of the produced weather fields, and for generating extreme weather fields. We increase the spatial variability by applying Direct Sampling as a post-processing step on the weather generator outputs. Furthermore, we produce out-of-sample extreme weather fields using Direct Sampling in two ways: 1) applying quantile mappings on the Direct Sampling simulations for a given return period, and 2) using a set of control points jointly with Direct Sampling with values informed by return period analysis. We validate our approach using precipitation, temperature, and cloud cover weather-fields time series datasets, for a region in northwest India. The results are analyzed using a set of statistical and connectivity metrics.
Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts across the world. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over the contiguous United States (CONUS), and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. Decreases in Northern Hemisphere summer frontal precipitation are largely driven by changes in the frequency of different front types, especially cold and stationary fronts. On the other hand, Northern Hemisphere winter exhibits some regional increases in frontal precipitation that are largely driven by changes in frontal precipitation intensity. While CONUS mean and extreme precipitation generally increase during all seasons in these climate change simulations, the likelihood of frontal extreme precipitation decreases, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.
This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5 and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a “warm-get-warmer” pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi-model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal-to-noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter-model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally-generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia.
The Terrestrial Gamma-ray Flashes (TGF) to lightning ratio, computed over the 3 tropical chimneys, presents a paradox: African thunderstorms produce the most lightning but yield the lowest fraction of TGF when compared to American and Southeast Asian thunderclouds. To understand the physical insights into this asymmetry, TRMM Precipitation Radar measurements are used to depict the vertical precipitation structure of the observed thunderstorms in the 3 regions and the thunderstorms during TGF occurrences detected by AGILE, Fermi-GBM and RHESSI sensors. African thunderstorms are taller, smaller and have higher concentration of dense ice particles above the freezing level. TGF thunderstorms are taller and less intense (0.5-2dBZ), besides presenting similar radar reflectivity decay with height independent of the region. In addition, these storms show thicker electrical charge layers separated by 4.7-5.2 km and also a positive charge fraction reduction between -20 o C and -40 o C and enhancement above -50 o C when compared to the overall thunderstorms.
River flow changes on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e. flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of overall flow regime. In this study, we used three analytical approaches to investigate three related questions: 1. how interrelated are flow regime metrics, 2. what catchment characteristics are most associated with flow regime at different timescales globally, and 3. what hydrological processes could explain these associations? To answer these questions, we analyzed a new global database of river discharge from 3,685 stations with coverage from 1987 to 2016. We calculated and condensed 189 traditional flow metrics via principal components analysis (PCA). We then used wavelet analysis to perform a frequency decomposition of each time series, allowing comparison with the flow metrics and characterization of variation in flow at different timescales across sites. Finally, we used three machine learning algorithms to relate flow regime to catchment properties, including climate, land-use, and ecosystem characteristics. For both the PCA and wavelet analysis, just a few catchment properties (catchment size, precipitation, and temperature) were sufficient to predict most aspects of flow regime across sites. The wavelet analysis revealed that variability in flow at short timescales was negatively correlated with variability at long timescales. We propose a hydrological framework that integrates these dynamics across daily to decadal timescales, which we call the Budyko-Darcy hypothesis.
The variability of the Southern Hemisphere (SH) extratropical large-scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state-of-the-art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150-day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal-mean circulation, and in hemispheric-scale precipitation and ocean surface wind stress. The 150-day period is consistent with the predictions of a new reduced-order model for the SAM, which suggests that this periodicity is tied with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM’s variability connect to the models’ ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.
In the coming decades, the frequency of coastal flooding will increase due to sea-level rise and changes in climate extremes. We force the Global Tide and Surge Model (GTSM) with a climate model ensemble from the CMIP6 High Resolution Model Intercomparison Project (HighResMIP) to produce global projections of extreme sea levels (defined as tides and storm surge) from 1950 to 2050. This is the first time that an ensemble of global ~25km resolution climate models is used for this purpose, which increases the credibility of projected storm surges. Here we validate the historical simulations (1985-2014) against the ERA5 climate reanalysis. The overall performance of the HighResMIP ensemble is good with mean bias smaller than 0.1 m. However, there is a strong large-scale spatial bias. Future projections for the high emission SSP5-8.5 scenario indicate changes up to 0.1 m or 20% in 10-year return period surge level from 1951-1980 to 2021-2050. Increases are seen in parts of the coastline of the Caribbean, Madagascar and Mozambique, Alaska, and northern Australia, whereas the Mediterranean region may see a decrease. The full dataset underlying this analysis, including timeseries and statistics, is openly available on the Climate Data Store and can be used to inform broad-scale assessment of coastal impacts under future climate change.
The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2021. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2021 was 1.89 K for the minimum daily temperature, 1.27 K for the mean, and 1.72 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 49% of the mean square error for the minimum temperature and 87% for the maximum. In a national application, we report a stronger relationship between minimum temperature in a heatwave and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.
WWLLN (World Wide Lightning Location Network) data on global lightning are used to investigate the increase of total lightning strokes at Arctic latitudes. We focus on the summertime data from June, July and August, which average >200,000 strokes each year above 65o North latitude, for each of the years from 2010 – 2020. The influence of WWLLN network detection efficiency increases is minimized by normalizing to the total global strokes for each northern summer. The ratio of strokes occurring above 65o increases with latitude, showing that the Arctic is becoming much more influenced by lightning. We compare the increasing fraction of strokes with the global temperature anomaly for those months, and find that the fraction of strokes above 65o to total global strokes for these months increases linearly with the temperature anomaly and grows by a factor of 3 as the anomaly increases from 0.65 to 0.95 degrees C.
Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.
Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.
Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Their seasonal development features fast and local changes in fractions of surface types demonstrating the necessity of improving melt pond fraction (MPF) products. We present a renewed method to extract MPF from Sentinel-2 satellite imagery, which is evaluated by MPF products from higher resolution satellite and helicopter-borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs amount to 17 % before and 23 % after the major drainage. Arctic-wide analysis of MPF for years 2017-2021 shows a consistent seasonal cycle in all regions and years.
The grandest geotourism attractions in the southern hemisphere, in the nineteenth century were the siliceous Pink and White Terraces, the lost New Zealand Eighth Wonder of the World. In 1886, the Tarawera eruption buried the terraces. In the absence of a government survey or evidence of their locations; public debate over their survival ensued until the 1940s. Recently, a unique survey was uncovered and led researchers at last to the Terrace locations. Early colonial visitors were told by traditional landowners, that the major White Terrace spring erupted in strong easterly winds. Having researched the Pink and White Terraces for some years, this 1859 report puzzled me, as it did Ferdinand Hochstetter to whom the first report was made in 1859. From previous studies in automotive crankcase ventilation, I could see a potential causal pathway for these east-wind spring eruptions. After examining the topography of the White Terrace spring, embankment and apron: I suggest the puzzling eruptions were a product of three phenomenae: the Venturi and Coandă effects, with Bernoulli’s principle. This paper presents the evidence for the presence of Venturi and Coandă effects at the Lake Rotomahana Basin. More importantly, it discusses how these effects contributed to postulated spring eruptions during the 1886 eruptions; which created so far unexplained water ponding around the Pink, Black and White Terrace locations. These surface waters contribute to the new paradigm for the Rotomahana Basin during the 1886 eruptions; where the topographic changes lead today’s researchers to the lost Terrace locations around the shores of the new Lake Rotomahana.
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM2.5) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM2.5 machine-learning model covering the contiguous US from 2003 through 2021. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. Averaging across all years in site-level cross-validation, the weighted mean absolute error of predictions (MAE) was 2.13 μg/m3, a substantial improvement over the mean absolute deviation from the median, which was 4.23 μg/m3. Comparing XIS to a leading product from the US Environmental Protection Agency, the Fused Air Quality Surface Using Downscaling (FAQSD), we obtained a 22% reduction in MAE. We also found a stronger relationship between PM2.5 and social vulnerability with XIS than with the FAQSD. Thus, XIS has potential for reconstructing environmental exposures, and its predictions have applications in environmental justice and human health.
Although adequately detailed kerosene chemical-combustion Arrhenius reaction-rate suites were not readily available for combustion modeling until ca. the 1990’s (e.g., Marinov ), it was already known from mass-spectrometer measurements during the early Apollo era that fuel-rich liquid oxygen + kerosene (RP-1) gas generators yield large quantities (e.g., several percent of total fuel flows) of complex hydrocarbons such as benzene, butadiene, toluene, anthracene, fluoranthene, etc. (Thompson ), which are formed concomitantly with soot (Pugmire ). By the 1960’s, virtually every fuel-oxidizer combination for liquid-fueled rocket engines had been tested, and the impact of gas phase combustion-efficiency governing the rocket-nozzle efficiency factor had been empirically well-determined (Clark ). Up until relatively recently, spacelaunch and orbital-transfer engines were increasingly designed for high efficiency, to maximize orbital parameters while minimizing fuels and structural masses: Preburners and high-energy atomization have been used to pre-gasify fuels to increase (gas-phase) combustion efficiency, decreasing the yield of complex/aromatic hydrocarbons (which limit rocket-nozzle efficiency and overall engine efficiency) in hydrocarbon-fueled engine exhausts, thereby maximizing system launch and orbital-maneuver capability (Clark; Sutton; Sutton/Yang). The combustion community has been aware that the choice of Arrhenius reaction-rate suite is critical to computer engine-model outputs. Specific combustion suites are required to estimate the yield of high-molecular-weight/reactive/toxic hydrocarbons in the rocket engine combustion chamber, nonetheless such GIGO errors can be seen in recent documents. Low-efficiency launch vehicles also need larger fuels loads to achieve the same launched mass, further increasing the yield of complex hydrocarbons and radicals deposited by low-efficiency rocket engines along launch trajectories and into the stratospheric ozone layer, the mesosphere, and above. With increasing launch rates from low-efficiency systems, these persistent (Ross/Sheaffer ; Sheaffer ), reactive chemical species must have a growing impact on critical, poorly-understood upper-atmosphere chemistry systems.
Being in an arid zone that is frequently submitted to high winds, south-central Arizona regularly gets impacted by several blowing dust events or dust storms every year. Major consequences of these events are visibility impairment and ensuing road traffic accidents, and a variety of health issues induced by inhalation of polluted air loaded with fine particulate matter produced by wind erosion. Despite such problems, and thus a need for guidance on mitigation efforts, studies dealing with dust source attribution for the region are largely missing. Furthermore, existing dust models exhibit large uncertainties and deficiencies in simulating dust events, rendering them of limited use in attribution studies or early warning systems. Therefore, to address some of these model issues, we have developed a high-resolution (1 km) dust modeling system by building upon an existing modeling framework consisting of Weather Research and Forecasting (WRF), FENGSHA (a dust emission model), and Community Multiscale Air Quality (CMAQ) models. In addition to incorporating new representations in the dust emission scheme, including roughness correction factor, sandblasting efficiency, and dust source mask, we implemented, in the dust model, up-to-date and very high-resolution data on land use, soil texture, and vegetation index. We used the revised dust modeling system to simulate a springtime dust storm (08–09 April 2013) of relatively long duration that caused a regional traffic incident involving minor injuries. The model simulations compared reasonably well against observations of concentration of particulate matter with a diameter of 10 μm and smaller (PM₁₀) and satellite-derived dust optical depth and vertical profile of aerosol subtypes. Interestingly, simulation results revealed that the anthropogenic (cropland) dust sources contributed more than half (~53 % or 260 µg/m³) of total PM₁₀, during the dust storm, over the region including Phoenix and western Pinal County. Contrary to the conventional wisdom that desert is the main dust source, our findings for this region challenge such belief and suggest that the regional air quality modeling over dryland regions should emphasize an improved representation of dust from agricultural lands as well, especially during high wind episodes. Such representations have the potential to inform decision-making in order to reduce windblown dust-related hazards on public health and safety.
Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus it is important to extract, analyze, and interpret this abundance of information in order to improve our understanding of the planetary bodies in our solar system and the role our planet’s geosphere played in the origin and evolution of life. Over the past decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad-hoc in nature. In order to systematize and synthesize the direction of these efforts, we introduce the concept of “Mineral Informatics”. Mineral Informatics is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics, the X-Informatics underpinnings that led to its conception, the needs, challenges, opportunities, and future directions. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and increased access to mineralogical data will increase cross- and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies.
The Central Highlands of Vietnam is the biggest Robusta coffee (Coffea canephora Pierre ex A.Froehner) growing region in the world. This study aims to identify the most important climatic variables that determine the current distribution of coffee in the Central Highlands and build a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A suitability model based on neural networks was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (i.e., 2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during harvest (October-November) and growing season (March-September) and decreasing precipitation during late growing season (July-September) mainly determined the loss in suitable areas. If the policy commitments made at the Paris agreement are met, the loss in coffee suitability could potentially be compensated by climate change adaptation measures such as making use of shade trees and adapted clones.