The projection of extreme convective precipitation by global climate models (GCM) exhibits significant uncertainty due to coarse resolutions. Direct dynamical downscaling (DDD) of regional climate at kilometer-scale resolutions provides valuable insight into extreme precipitation changes, but its computational expense is formidable. Here we document the effectiveness of machine learning to enable smart dynamical downscaling (SDD), which selects a small subset of GCM data to conduct downscaling. Trained with data for three subtropical/tropical regions, convolutional neural networks (CNNs) retained 92% to 98% of extreme precipitation events (rain intensity higher than the 99th percentile) while filtering out 88% to 95% of circulation data. When applied to reanalysis data sets differing from training data, the CNNs’ skill in retaining extremes decreases modestly in subtropical regions but sharply in the deep tropics. Nonetheless, one of the CNNs can still retain 62% of all extreme events in the deep tropical region in the worst case.
Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave radiances using Hurricane Harvey (2017) as an example. Assimilating GPM constellation all-sky microwave radiances in addition to GOES-16 all-sky infrared radiances reduces the forecast errors in the TC track, rapid intensification, and peak intensity compared to assimilating all-sky infrared radiances alone, including a 24-hour increase in forecast lead-time for rapid intensification. Assimilating all-sky microwave radiances also improves Harvey’s hydrometeor fields, which leads to improved forecasts of rainfall after Harvey’s landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.
The extreme variability of the cold point tropopause temperature (TCPT) and height (HCPT) are examined over a tropical station, Gadanki (13.45N, 79.2E) using high-resolution radiosonde data during the period 2006-2014. The extreme variabilities such as the coldest (warmest) tropopause is defined if TCPT is lesser (greater) than the lower (upper) limit of its two-sigma level whereas the highest (lowest) tropopause is defined as the HCPTis greater (lesser) than the lower (upper) limit of its two-sigma level. In total 161 extreme cases such as the coldest (52) and warmest (30) TCPT and the highest (57) and lowest (22) HCPT are observed. The coldest (187.2±1.60 K, 17.3±0.52 km), warmest (194.2±1.78 K, 16.9±0.89 km), lowest (191.7±1.78 K, 18.2±0.55 km) and highest (191.8±2.11 K, 16.2±0.38 km) occurs without preference of season. These extreme tropopause cases occur independently. Thermal structure of the coldest tropopause cases reveals that they are often sharper whereas the warmest, highest and lowest tropopause is broader. Water vapor and ozone concentrations are found to be high for the warmest tropopause and low for the coldest tropopause. Under the shallow convection, extreme temperature profiles, in general, show prominent warming between 8-14 km while anomalous cooling (warming) just below (above) the CPT. The occurrence of the tropical cyclones, cirrus clouds and equatorial wave propagation are the possible candidates for the occurrence of the extreme tropopauses.
Evaluating the influence of anthropogenic emissions changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emissions changes. However, the ability of these widely-used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using two scenarios simulated by a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic emissions changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely-used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local and regional scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant emission input and quantify the degree to which emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the effectiveness of emissions reduction policies using statistical approaches.
El Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central-eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, by objectively optimizing the RO equations fit to observations, we develop an improved recharge index. (1) Te-variability is regressed out, building h_ind statistically-independent from Te. Capturing the pure recharge, h_ind reconciles usual indices. (2) The optimum is equatorial plus southwestern Pacific h_ind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using h_ind_eq+sw, the RO becomes more consistent with observations. h_ind_eq+sw is more relevant for ENSO operational diagnostics.
This paper describes the first implementation of the d x=3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40 day prescribed-sea-surface-temperature simulation (Jan 20-Feb 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) phase 2 model intercomparison. Effective resolution is found to be $\sim 6x the horizontal grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long-standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g. hurricanes, midlatitude storms including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.
A multivariate functional principal component analysis (PCA) approach to the full-track simulation of tropical cyclones (TCs) is developed for risk assessment. Elemental variables of TC along the track necessary for risk assessment, such as center coordinates, maximum wind speed, minimum central pressure and ordinal dates, can be simulated simultaneously at one go, using solely the best-track data with no data supplemented from any other sources. The simulation model is optimally determined by means of the ladle estimator. A TC occurrence model using the Conway–Maxwell–Poisson distribution is proposed as well, by which different dispersion features of annual occurrence can be represented in a unified manner. With the occurrence model, TCs can be simulated on an annual basis. The modeling and simulation process is programmed and fully automated such that little manual intervention is required, which greatly improves the modeling efficiency and reduces the turnaround time, especially when newly available TC data are incorporated periodically into the model. Comprehensive evaluation shows that this approach is capable of generating high-performance synthetic TCs in terms of distributional and extreme value features, which can be used in conjunction with wind field and engineering vulnerability models to estimate economic and insurance losses for governments and insurance/reinsurance industry.
Marine heatwaves (MHWs) are extreme oceanic warm water events (above 90th percentile threshold) that significantly impact the marine environment. Several studies have recently explored the genesis and impacts of MHWs though they are least understood in the tropical Indian Ocean. Here we investigate the genesis and trend of MHWs in the Indian Ocean during 1982–2018 and their role in modulating the Indian monsoon. We find that the rapid warming in the Indian Ocean plays a critical role in increasing the number of MHWs. Meanwhile, the El Nino has a prominent influence on the occurrence of MHWs during the summer monsoon. The Indian Ocean warming and the El Nino variability have synergistically resulted in some of the strongest and long-lasting MHWs in the Indian Ocean. The western Indian Ocean (WIO) region experienced the largest increase in MHWs at a rate of 1.2–1.5 events per decade, followed by the north Bay of Bengal at a rate of 0.4–0.5 events per decade. Locally, the MHWs are induced by increased solar radiation, relaxation of winds, and reduced evaporative cooling. In the western Indian Ocean, the decreased winds further restrict the heat transport by ocean currents from the near-equatorial regions towards the north. Our analysis indicates that the MHWs in the western Indian Ocean and the north Bay of Bengal lead to a reduction in monsoon rainfall over the central Indian subcontinent. On the other hand, there is an enhancement of monsoon rainfall over southwest India due to the MHWs in the Bay of Bengal.
This study used data of 165 rain stations for mapping SPI index for winter season at the base of 7 months period (from November to next April). For compensating the lost measurements in stations, we used the Kriging method in ArcGIS10.1 for producing precipitation maps for each period of the year. We used a simple equation for calculating SPI 7 , and modeled the process by using Model Maker tool within the ERDAS 2014. The application of this model over the period from 1992 to 2018 produced twenty-seven annual maps of the SPI 7 in Syria. By plotting the general trend of precipitation changes according to this index, we observed that the entire study area is stable on a normal climatic state close to the annual average during the most of years. By analyzing precipitation and SPI maps using statistical zonal methods, based on agricultural stabilization zones, we found different behaviors of drought in every zone and between these zones.
Most tree species predominantly associate with a single type of mycorrhizal fungi, which can differentially affect plant nutrient acquisition and biogeochemical cycling. Here, we address for the first time the impact of mycorrhizal distributions on global carbon and nutrient cycling. Using the state-of-the-art carbon-nitrogen economics within the Community Land Model version 5 (CLM5) we found Net Primary Productivity (NPP) increased throughout the 21st century by 20%; however, as soil nitrogen has progressively become limiting, the costs to NPP for nitrogen acquisition — i.e., to mycorrhizae — have increased at a faster rate by 60%. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated carbon to support the same productivity. Uncertainties in mycorrhizal distributions are non-trivial, however, with uncertainties in NPP by up to 345 Tg C yr-1, depending on which published distribution is used. Remote sensing capabilities for mycorrhizal detection show promise for refining these estimates further.
The characterization of changes over the full distribution of precipitation intensities remains an overlooked and underexplored subject, despite their critical importance to hazard assessments and water resource management. Here, we aggregate daily in situ Global Historical Climatology Network precipitation observations within seventeen internally consistent domains in the United States for two time periods (1951-1980 and 1991-2020). We find statistically significant changes in wet day precipitation distributions in all domains – changes primarily driven by a shift from lower to higher wet day intensities. Patterns of robust change are geographically consistent, with increases in the mean (4.5-5.7%) and standard deviation (4.4-8.7%) of wet day intensity in the eastern U.S., but mixed signals in the western U.S. Beyond their critical importance to the aforementioned impact assessments, these observational results can also inform climate model performance evaluations.
Public health risks resulting from urban heat in cities are increasing due to rapid urbanisation and climate change, motivating closer attention to urban heat mitigation and adaptation strategies that enable climate-sensitive urban design and development. These strategies incorporate four key factors influencing heat stress in cities: the urban form (morphology of vegetated and built surfaces), urban fabric, urban function (including human activities), and background climate and regional geographic settings (e.g. topography and distance to water bodies). The first two factors can be modified and redesigned as urban heat mitigation strategies (e.g. changing the albedo of surfaces, replacing hard surfaces with pervious vegetated surfaces, or increasing canopy cover). Regional geographical settings of cities, on the other hand, cannot be modified and while human activities can be modified, it often requires holistic behavioural and policy modifications and the impacts of these can be difficult to quantify. When evaluating the effectiveness of urban heat mitigation strategies in observational or traditional modelling studies, it can be difficult to separate the impacts of modifications to the built and natural forms from the interactions of the geographic influences, limiting the universality of results. To address this, we introduce a new methodology to determine the influence of urban form and fabric on thermal comfort, by utilising a comprehensive combination of possible urban forms, an urban morphology data source, and micro-climate modelling. We perform 9814 simulations covering a wide range of realistic built and natural forms (building, roads, grass, and tree densities as well as building and tree heights) to determine their importance and influence on thermal environments in urban canyons without geographical influences. We show that higher daytime air temperatures and thermal comfort indices are strongly driven by increased street fractions, with maximum air temperatures increases of up to 10 and 15◦C as street fractions increase from 10% (very narrow street canyons and/or extensive vegetation cover) to 80 and 90% (wide street canyons). Up to 5◦C reductions in daytime air temperatures are seen with increasing grass and tree fractions from zero (fully urban) to complete (fully natural) coverage. Similar patterns are seen with the Universal Thermal Climate Index (UTCI), with increasing street fractions of 80% and 90% driving increases of 6 and 12◦C, respectively. We then apply the results at a city-wide scale, generating heat maps of several Australian cities showing the impacts of present day urban form and fabric. The resulting method allows mitigation strategies to be tested on modifiable urban form factors isolated from geography, topography, and local weather conditions, factors that cannot easily be modified.
Seasonal prediction is one important element in a seamless prediction chain between weather forecast and climate projections. After several years of common development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990-2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has doubled for the North Atlantic Oscillation index. During boreal summer, overall a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed. Future developments for climate forecasts need a stronger focus on the performance of seasonal dependent processes in a model system.
In this study, a storm surge model of the semi-enclosed Tokyo Bay was constructed to investigate its hydrodynamic response to major typhoon parameters, such as the point of landfall, approach angle, forward speed, size, and intensity. The typhoon simulation was validated for Typhoon Lan in 2017, and 31 hypothetical storm surge scenarios were generated to establish the sensitivity of peak surge height to the variation in typhoon parameters. The maximum storm surge height in the upper bay adjacent to the Tokyo Metropolitan Area was found to be highly sensitive to the forward speed and size of the passing typhoon. However, the importance of these parameters in disaster risk reduction has been largely overlooked by researchers and disaster managers. It was also determined that of the many hypothetical typhoon tracks evaluated, the slow passage of a large and intense typhoon transiting parallel to the longitudinal axis of Tokyo Bay, making landfall 25 km southwest, is most likely to cause a hazardous storm surge scenario in the upper-bay area. The results of this study are expected to be useful to disaster managers for advanced preparation against destructive storm surges.
Urban overheating, driven by global climate change and urban development, is a major contemporary challenge which substantially impacts urban livability and sustainability. Overheating represents a multi-faceted threat to well-being, performance, and health of individuals as well as the energy efficiency and economy of cities, and it is influenced by complex interactions between building, city, and global scale climates. In recent decades, extensive discipline-specific research has characterized urban heat and assessed its implications on human life, including ongoing efforts to bridge neighboring disciplines. The research horizon now encompasses complex problems involving a wide range of disciplines, and therefore comprehensive and integrated assessments are needed that address such interdisciplinarity. Here, the objective is to go beyond a review of existing literature and provide a broad overview and future outlook for integrated assessments of urban overheating, defining holistic pathways for addressing the impacts on human life. We (i) detail the characterization of heat exposure across different scales and in various disciplines, (ii) identify individual sensitivities to urban overheating that increase vulnerability and cause adverse impacts in different populations, (iii) elaborate on adaptive capacities that individuals and cities can adopt, (iv) document the impacts of urban overheating on health and energy, and (v) discuss frontiers of theoretical and applied urban climatology, built environment design, and governance toward reduction of heat exposure and vulnerability at various scales. The most critical challenges in future research and application are identified, targeting both the gaps and the need for greater integration in overheating assessments.
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.
This paper presents for the first time results on winds, tides, gradients of horizontal winds, and momentum fluxes at mesosphere and lower thermosphere (MLT) altitudes over southern Patagonia, one of the most dynamically active regions in the world. For this purpose, measurements provided by SIMONe Argentina are investigated. SIMONe Argentina is a novel multistatic specular meteor radar system that implements a SIMONe (Spread Spectrum Interferometric Multistatic meteor radar Observing Network) approach, and that has been operating since the end of September 2019. Average counts of more than 30000 meteor detections per day result in tidal estimates with statistical uncertainties of less than 1 m/s. Thanks to the multistatic configuration, horizontal and vertical gradients of the horizontal winds are obtained, as well as vertical winds free from horizontal divergence contamination. The vertical gradients of both zonal and meridional winds exhibit strong tidal signatures. Mean momentum fluxes are estimated after removing the effects of mean winds using a four-hour, eight-kilometer window in time and altitude, respectively. Reasonable statistical uncertainties of the momentum fluxes are obtained after applying a 28-day averaging. Therefore, the momentum flux estimates presented in this paper represent monthly mean values of waves with periods of four hours or less, vertical wavelengths shorter than eight kilometers, and horizontal scales less than 400 km.
The paper presents a combined numerical - deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. It resulted in ∼10% RMSE improvement in both wind velocity and wave height forecasts over operational models. This significant improvement is even more substantial at the Aegean Sea from May to September, when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either numerical models. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs.