Arctic spring and summertime aerosol optical depth (AOD) baseline from long-term observations and model reanalyses: climatology, trend and extreme event statistics
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Peng Xian↗
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Arctic Spring and Summertime Aerosol Extreme Events: Statistics and Implications for the Impact of Regional Biomass Burning Processes
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Peng Xian↗
North American Extreme Temperature Events and Related Large Scale Meteorological Patterns: A Review of Statistical Methods, Dynamics, Modeling, and Trends
The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and landatmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.
Cold Spells↗
Global Statistical Maps of Extreme-Event Magnetic Observatory 1 Min First Differences in Horizontal Intensity
Analysis is made of the long-term statistics of three different measures of ground level, storm time geomagnetic activity: instantaneous 1 min first differences in horizontal intensity (delta)Bh, the root-mean-square of 10 consecutive 1 min differences S, and the ramp change R over 10 min. Geomagnetic latitude maps of the cumulative exceedances of these three quantities are constructed, giving the threshold(nTmin) for which activity within a 24 h period can be expected to occur once per year, decade, and century. Specifically, at geomagnetic 55deg, we estimate once-per-century (delta)Bh, S, and R exceedances and a site-to-site,proportional, 1 standard deviation range [1(sigma), lower and upper] to be, respectively, 1000, [690, 1450]; 500,[350, 720]; and 200, [140, 280] nTmin. At 40deg, we estimate once-per-century (delta)Bh, S, and R exceedances and1(sigma) values to be 200, [140, 290]; 100, [70, 140]; and 40, [30, 60] nTmin.
Love, Jeffrey J.↗
The Relationships Between the Trends of Mean and Extreme Precipitation
This study provides a better understanding of the relationships between the trends of mean and extreme precipitation in two observed precipitation data sets: the Climate Prediction Center Unified daily precipitation data set and the Global Precipitation Climatology Program (GPCP) pentad data set. The study employs three kinds of definitions of extreme precipitation: (1) percentile, (2) standard deviation and (3) generalize extreme value (GEV) distribution analysis for extreme events based on local statistics. Relationship between trends in the mean and extreme precipitation is identified with a novel metric, i.e. area aggregated matching ratio (AAMR) computed on regional and global scales. Generally, more (less) extreme events are likely to occur in regions with a positive (negative) mean trend. The match between the mean and extreme trends deteriorates for increasingly heavy precipitation events. The AAMR is higher in regions with negative mean trends than in regions with positive mean trends, suggesting a higher likelihood of severe dry events, compared with heavy rain events in a warming climate. AAMR is found to be higher in tropics and oceans than in the extratropics and land regions, reflecting a higher degree of randomness and more important dynamical rather than thermodynamical contributions of extreme events in the latter regions.
global warming↗
MERRA-2 Data and Analytic Services at NASA GES DISC for Climate Extremes Study
NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.
data access↗
Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.
Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.
Storm↗
North American Extreme Precipitation Events and Related Large-Scale Meteorological Patterns: a Review of Statistical Methods, Dynamics, Modeling, and Trends
This paper surveys the current state of knowledge regarding Large-Scale Meteorological Patterns (LSMPs) associated with short-duration (less than one week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some 4 promising analysis techniques have been identified and the LSMP perspective is useful for evaluating model dynamics.
Mathew Barlow↗
Synoptic Scale Influences on Increasing Summertime Extreme Precipitation Events in the Northeastern United States
Over the past 15 years, the northeastern United States has seen a statistically significant increase in the frequency of extreme precipitation events that is larger and more widespread than anywhere else in the country. This increase in events is more likely to be associated with frontal and low-pressure systems, rather than being caused by more tropical cyclones impacting the region.
Extreme Precipitation↗
Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis
Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.
Monsoon↗
Accurately Characterizing the Importance of Wave-Particle Interactions in Radiation Belt Dynamics: The Pitfalls of Statistical Wave Representations
Wave-particle interactions play a crucial role in energetic particle dynamics in the Earths radiation belts. However, the relative importance of different wave modes in these dynamics is poorly understood. Typically, this is assessed during geomagnetic storms using statistically averaged empirical wave models as a function of geomagnetic activity in advanced radiation belt simulations. However, statistical averages poorly characterize extreme events such as geomagnetic storms in that storm-time ultralow frequency wave power is typically larger than that derived over a solar cycle and Kp is a poor proxy for storm-time wave power.
Murphy, Kyle R.↗
Statistical Approach for Determining the Onsets/Durations of ENSO Cycle Extremes
During the interval of 1950-mid 1998, some 16 El Nino and 10 La Nina have been identified on the basis of sea surface temperature in the Nino 3.4 region, these 26 events representing the extremes of the quasi-periodic ENSO cycle. Statistical aspects of these events are examined. Surprisingly, the durations of El Nino and La Nina appear to be strongly bifurcated into shorter and longer duration classes, as do the recurrence periods of El Nino. Moreover, the duration of an El Nino appears to provide a statistically meaningful indication as to when to expect the next onset of El Nino. Because the last El Nino had its onset in April 1997 and was of longer duration, onset of the next El Nino, probably, will not occur until after February 2000.
Wilson, Robert M.↗
Trend Estimates of AERONET-Observed and Model-Simulated AOTs Between 1993 and 2013
Recently, temporal changes in Aerosol Optical Thickness (AOT) have been investigated based on model simulations, satellite and ground-based observations. Most AOT trend studies used monthly or annual arithmetic means that discard details of the generally right-skewed AOT distributions. Potentially, such results can be biased by extreme values (including outliers). This study additionally uses percentiles (i.e., the lowest 5%, 25%, 50%, 75% and 95% of the monthly cumulative distributions fitted to Aerosol Robotic Network (AERONET)-observed and ECHAM/MESSy Atmospheric Chemistry (EMAC)-model simulated AOTs) that are less affected by outliers caused by measurement error, cloud contamination and occasional extreme aerosol events. Since the limited statistical representativeness of monthly percentiles and means can lead to bias, this study adopts the number of observations as a weighting factor, which improves the statistical robustness of trend estimates. By analyzing the aerosol composition of AERONET-observed and EMAC-simulated AOTs in selected regions of interest, we distinguish the dominant aerosol types and investigate the causes of regional AOT trends. The simulated and observed trends are generally consistent with a high correlation coefficient (R = 0.89) and small bias (slope+/-2(sigma) = 0.75 +/- 0.19). A significant decrease in EMAC-decomposed AOTs by water-soluble compounds and black carbon is found over the USA and the EU due to environmental regulation. In particular, a clear reversal in the AERONET AOT trend percentiles is found over the USA, probably related to the AOT diurnal cycle and the frequency of wildfires. In most of the selected regions of interest, EMAC-simulated trends are mainly attributed to the significant changes of the dominant aerosols; e.g., significant decrease in sea salt and water soluble compounds over Central America, increase in dust over Northern Africa and Middle East, and decrease in black carbon and organic carbon over Australia.
AERONET↗
The Extreme Universe Space Observatory
This talk will describe the Extreme Universe Space Observatory (EUSO) mission. EUSO is an ESA mission to explore the most powerful energy sources in the universe. The mission objectives of EUSO are to investigate EECRs, those with energies above 3x10(exp 19) eV, and very high-energy cosmic neutrinos. These objectives are directly related to extreme conditions in the physical world and possibly involve the early history of the big bang and the framework of GUTs. EUSO tackles the basic problem posed by the existence of these extreme-energy events. The solution could have a unique impact on fundamental physics, cosmology, and/or astrophysics. At these energies, magnetic deflection is thought to be so small that the EECR component would serve as the particle channel for astronomy. EUSO will make the first measurements of EAS from space by observing atmospheric fluorescence in the Earth's night sky. With measurements of the airshower track, EUSO will determine the energy and arrival direction of these extreme-energy events. EUSO will make high statistics observations of CRs beyond the predicted GZK cutoff energy and widen the channel for high-energy neutrino astronomy. The energy spectra, arrival directions, and shower profiles will be analyzed to distinguish the nature of these events and search for their sources. With EUSO data, we will have the possibility to discover a local EECR source, test Z-burst scenarios and other theories, and look for evidence of the breakdown of the relativity principle at extreme Lorentz factors.
Adams, Jim↗
Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events
This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.
Trace gas↗
Perspectives on Forced Predictability
This talk addresses the relationships between weather and El Nino/Southern Oscillation (ENSO) and various other low frequency atmospheric variations such as the Pacific/North American pattern. We focus, in particular, on the predictability of extreme wintertime precipitation events over the continental United States. We first examine fifty years of daily precipitation observations and quantify the leading patterns of winter weather variability. These weather systems, familiar to operational weather forecasters, account for many of the major flooding events on the west coast as well as the major winter snowstorms along the east coast. We next examine the statistics of these storm systems with a particular focus on the occurrence of extreme events. The leading weather systems show varying degrees of linkages to low frequency atmospheric variability. We show, for example, that on seasonal time scales the probability of occurrence of certain types of west coast storms is strongly enhanced by El Nino, while it is reduced during winters with an anomalous trough to the west of the dateline. ENSO also has a strong impact on storms that develop over the Gulf of Mexico and affect much of the eastern United States, with enhanced storminess during El Nino, and reduced storminess during La Nina conditions. We conclude with an example of how well current climate models are able to reproduce the basic linkages between weather and low frequency variability.
Schubert, S.↗
On the Annual Frequency of Intense Hurricanes in Relation to the Extremes of ENSO and the Interludes Between Them
On the basis of Trenberth's quantitative definition for marking the occurrence of an El Nino, one can precisely identify some 15 El Nino and 10 La Nina events during the interval of 1950-1997. The statistical aspects of these identified extremes and the intervening periods between them (interludes) are determined. Also, a comparison of the appearances of these extremes and interludes with yearly hurricane seasons shows that of the last 48 hurricane seasons, 20 (42%) are El Nino-related (i.e., an El Nino was in progress during all, or part, of the yearly hurricane season - June-November), 13 (27%) are La Nina-related, and 15 (31%) are interlude - related. Combining the two subgroups of La Nino- and interlude - related seasons into a single grouping called non-El Nino-related seasons, one finds that the non-El Nino-related seasons have a mean annual frequency of intense hurricanes measuring 2.8, while the El Nino-related seasons have a mean annual frequency of intense hurricanes measuring 1.3, and the observed difference in the means is inferred to be statistically important at the 99.8% level of confidence. Therefore, during El Nino-related seasons, fewer intense hurricanes (typically, less than or equal to 2) should be expected, while durin(y non-El Nino-related seasons, more (typically, greater than or equal to 2) should be expected. Implications for the upcoming 1998 and future hurricane seasons are discussed.
Wilson, Robert M.↗
Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil
Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.
IMERG↗