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Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

Observation-based climate model evaluation and future projections help policymakers in developing action plans for efficient management of water resources and mitigation of the impacts of hazardous extremes. Apart from this socioeconomic importance, the scientific value cannot be overstated, especially in light of the upcoming Fifth U.S. National Climate Assessment (NCA) report. In this study, we evaluate the realism of hydroclimate variability in the historical simulations of a suite of coupled general circulation models (CGCMs) participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Our results demonstrate systematic biases in the simulated seasonal precipitation—most prominently, wet bias over the mountainous western United States in winter, and dry bias over the U.S. central plains in summer. A distinctive feature of this work is our focus on the examination of the atmospheric water budget, in particular the relative importance of remote and local contributions—convergence of moisture fluxes and local land surface processes (evapotranspiration) respectively—in helping produce precipitation. This diagnosis reveals that the leading contribution of the remote influence in winter is overestimated by the CMIP6 multimodel mean (MMM), whereas the local influence, which is more influential in summer, is underestimated. Our results aid in understanding the drivers of seasonal precipitation over the United States, where precipitation will likely increase by the end of the century but with significant model disagreement for the summer and fall. In support of ongoing NCA efforts, our study aims to contribute a comprehensive, regional-level analysis of the moisture budget and emphasizes the importance of realistically simulating its major components in CGCMs.

Recent extreme heat events, especially those that have occurred in the Western United States (WUS), are fueling wildfires and funneling smoke at an unprecedented level, impacting air/water quality and leading to an increase in respiratory hospitalizations. Under greenhouse warming, extreme weather conditions that favor wildfire ignition are expected to occur more frequently over the contiguous United States (CONUS). Therefore, predicting wildfire danger under a changing climate is essential in managing future wildfires and protecting the welfare of people and the environment. In response to mitigating wildfire risks, the Canadian Fire Weather Index (FWI) was developed to provide a numeric rating representing the intensity of a spreading fire. In this work, we utilized fine-scale (0.25° x 0.25°) daily meteorological inputs from thirty-five general circulation models in NASA Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) data to calculate the FWI. Using the daily maximum temperature, relative humidity, wind speed, and precipitation from NEX-GDDP-CMIP6, we calculated the FWI of historical and future simulations from the periods of 1950 to 2100 under different emission scenarios (Shared Socioeconomic Pathways 2-4.5 and 5-8.5). We have analyzed the FWI for the GISS-E2-1-G model, which indicates a 2-3% increase per decade in future fire danger under both emission-pathway-driven climate scenarios during the dry season in the Southwestern US. We have found that the FWI climatology in the Southwestern US during the Summer presents high to extreme fire danger (> 50) and higher FWI values in the future compared to historical observations. Moreover, we have explored the uncertainties across multiple models using NEX-GDDP-CMIP6 statistically downscaled data and found a significant spread of the FWI across the models for historical observations and future simulations. To correlate the link between the FWI and actual fire occurrence, we will calculate the FWI using reanalysis data (MERRA-2) and validate the FWI with actual fire occurrence data from Global Fire Emissions Database (GFED) with a special emphasis on the WUS. While supporting the US NCA and NASA’s Climate Adaptation Service Investigator (CASI), we will also try to contribute FWI to NASA’s FireSense, an initiative to bring an Earth systems approach to improving wildfire and wildland fire management.

Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting, and their resulting ensemble means, to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and their statistically downscaled counterparts created with the Localized Canonical Analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that model weights and corresponding weighted projections are highly sensitive to the weighting method as well as to the selected variables and spatial domains. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting schemes. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. If future impact assessments utilize weighting schemes, then our findings suggest that how the weighting scheme is derived and applied to the projections may depend on the needs of an impact assessment or adaptation plan. From the results of our analysis, we summarize our recommendations concerning multi-model ensemble weighting as follows: Weighted ensemble means should be used not only for national and international assessments but also for regional impacts assessments and planning. Multiple strategies for model weighting are employed when feasible, to assure that uncertainties from various sources (e.g., weighting strategy used, domain or variable of interest applied, etc.) are considered. That weighting is derived for individual sub-regions (such as the NCA regions) in addition to what is derived for the continental United States. That domain-specific weighting be derived using both common (e.g. precipitation) and stakeholder-specific (e.g. streamflow) variables to produce relevant analysis for impact assessments and planning.

This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.

A 30-yr climatology of lightning days and associated synoptic meteorological patterns are characterized across the interior western United States (WUS). Locally centered composite analyses show preferred synoptic meteorological patterns with positive 500-hPa geopotential height anomalies located to the northeast and negative sea level pressure anomalies to the northwest and collocated with local lightning days. Variations in preferred patterns for local lightning days are seen across the interior WUS. Areas not commonly affected by the North American monsoon system including the western Great Basin and northern Rocky Mountains show higher-amplitude anomalies of geopotential height, moisture, and midtropospheric instability patterns suggesting the importance of episodic midlatitude dynamics to lightning days in such locations. By contrast, locations closer to the core of the North American monsoon show weaker anomalies, likely reflecting the prevalence of favorable mesoscale dynamics key to lightning production during warm-season months in locations in the interior Southwest. Meteorological patterns for select locations are explored in more detail and two case studies of notably active lightning events are presented. Results from this observational analysis provide a foundation for evaluating meteorological conditions on lightning days in climate model simulations for the interior WUS.

Thermal infrared spectra can provide unique information on atmospheric ammonia enhancements, including those produced by agricultural, industrial, and biomass burning sources. In this work, we develop and test a quantitative ammonia retrieval algorithm using data from the airborne hyperspectral thermal emission spectrometer (HyTES) instrument over diverse targets, including persistent ammonia sources, such as cattle feedlots and a power plant, as well as a small, smoldering wildfire. Although no formal validation was possible due to lack of collocated observations, the HyTES ammonia values over feedlots in the California San Joaquin Valley are shown to be consistent within the estimated retrieval uncertainty of $\sim$ 20%–50% with in situ ammonia values collected over the same facilities during the 2013 DISCOVER-AQ field campaign. HyTES also observed meter-scale thermal hot spots associated with a small wildfire in Northern Arizona, which was determined to coincide with the emission of free ammonia, presumed to originate from the fire. This very localized emission source, not visible from satellite sensors, could be resolved in HyTES imagery. The ammonia enhancement immediately downwind from the smoldering fire, before the free ammonia reacted to form aerosols, was found to be distinguishable from the background with values three to ten times smaller than ammonia enhancements from the power plant and cattle feedlots, respectively. High-resolution detection and quantification of ammonia over nonpersistent sources like wildfires from HyTES shows that it is a powerful tool that can be used to improve regional scale fire emission inventories by accounting for the presence of temporally short ammonia processes.

Given that persons with a prior history of respiratory diseases tend to demonstrate more severe illness from COVID-19 and, hence, are at higher risk of serious symptoms, ambient air quality data from NASA's satellite observations might provide a critical insight into which geographical areas may exhibit higher numbers of hospitalizations due to COVID-19, how the expected severity of COVID-19 and associated survival rates may vary across space in the future, and most importantly how given this information, health professionals can distribute vaccines in a more efficient, timely, and fair manner. Despite the utmost urgency of this problem, there yet exists no systematic analysis on linkages among COVID-19 clinical severity, air quality, and other atmospheric conditions, beyond relatively simplistic regression-based models. The goal of this project is to glean a deeper insight into sophisticated spatio-temporal dependencies among air quality, atmospheric conditions, and COVID-19 clinical severity using the machinery of Geometric Deep Learning (GDL), while providing quantitative uncertainty estimates. Our results based on the GDL model on a county level in three US states, California, Pennsylvania and Texas, indicate that AOD attributes to COVID-19 clinical severity in 39, 30, and 132 counties out of 58, 67, and 254 total counties, respectively. In turn, relative humidity is another important factor for understanding dynamics of clinical course and mortality risks due COVID-19, but predictive utility of temperature is noticeably lower. Our findings do not only contribute to understanding of latent factors behind COVID-19 progression but open new perspectives for innovative use of NASA's datasets for biosurveillance and social good.

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability.

Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

Increasing severity of extreme heat is a hallmark of climate change. Its impacts depend on temperature but also on moisture and solar radiation, each with distinct spatial patterns and vertical profiles. Here, we consider these variables’ combined effect on extreme heat stress, as measured by the environmental stress index, using a suite of high-resolution climate simulations for historical (1980–2005) and future (2074–2099, Representative Concentration Pathway 8.5 (RCP8.5)) periods. We find that observed extreme heat stress drops off nearly linearly with elevation above a coastal zone, at a rate that is larger in more humid regions. Future projections indicate dramatic relative increases whereby the historical top 1% summer heat stress value may occur on about 25%–50% of future summer days under the RCP8.5 scenario. Heat stress increases tend to be larger at higher latitudes and in areas of greater temperature increase, although in the southern and eastern US moisture increases are nearly as important. Imprinted on top of this dominant pattern we find secondary effects of smaller heat stress increases near ocean coastlines, notably along the Pacific coast, and larger increases in mountains, notably the Sierra Nevada and southern Appalachians. This differential warming is attributable to the greater warming of land relative to ocean, and to larger temperature increases at higher elevations outweighing larger water-vapor increases at lower elevations. All together, our results aid in furthering knowledge about drivers and characteristics that shape future extreme heat stress at scales difficult to capture in global assessments.

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distributions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, temperature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quantifiable structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of confirmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.

A multivariate assessment of climate model projections over South America from the CMIP5 archive is presented. Change in near-surface temperature, precipitation, evapotranspiration, integrated water vapor transport (IVT), sea level pressure, and wind at multiple pressure levels is quantified across the multi-model suite and an assessment of model-to-model agreement on projected change performed. All models project warming by the mid- and late-21st century throughout the continent, with the highest magnitude projected over tropical regions. The CMIP5 models are in strong agreement that precipitation will decrease in all seasons over portions of Patagonia, especially along the northern portions of the current-climate mid-latitude storm track. This is consistent with a robustly projected poleward shift of the Pacific extratropical high and mid-latitude storm track indicated by a systematic increase in sea level pressure and decrease in westerly wind over Patagonia. Decreased precipitation for the months of September, October, and November is also projected, with strong model agreement, over portions of northern and northeastern Brazil, coincident with decreases in sea level pressure and increases in evapotranspiration. IVT is broadly projected to decrease over southern South America, coincident with the projected poleward shift of the mid-latitude storm track indicators, with increases projected in the vicinity of the South Atlantic Convergence Zone in austral spring and summer. Further decomposition of the thermodynamic and dynamic components to this change in IVT indicate that the projected decreases in the mid-latitudes are primarily driven by changes in circulation (i.e. dynamic) while the sub-tropical and tropical changes have a predominantly thermodynamic origin. Results provide a comprehensive picture of climate change across South America and highlight where projections should be interpreted with the most confidence.

The need to better understand climate change has driven model simulations to greater fidelity with improved spatiotemporal resolution (e.g., < 10 km at sub-hourly cadence). For example, the 7 km GE...

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The Level 2 (swath) product, which is reported on a 4.4 km spatial grid, is designated Version 23 (V23) and contains retrieved aerosol optical depth (AOD) and aerosol particle property information derived from MISR's multi-angle observations over both land and water. The changes from the previous version of the algorithm (V22) have significant impacts on the data product and its interpretation. The V23 data set is created from two separate retrieval algorithms that are applied over dark water and land surfaces, respectively. Besides increasing the horizontal resolution to 4.4 km compared with the coarser 17.6 m resolution in V22, and streamlining the format and content, the V23 product has added geolocation information, pixel-level uncertainty estimates, and improved cloud screening. MISR data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://eosweb.larc.nasa.gov/project/misr/misr_table. The version number for the V23 Level 2 aerosol product is F13_0023. The Level 3 (gridded) aerosol product is still reported at 0.5° x 0.5° spatial resolution with results aggregated from the higher-resolution Level 2 data. The format and content at Level 3 have also been updated to reflect the changes made at Level 2. The Level 3 product associated with the V23 Level 2 product version is designated F15_0032. Both the Level 2 and Level 3 products are now provided in NetCDF format.

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmöller diagram, and diurnal spatial pattern over the USGP from the simulations focusing on the summer precipitation diurnal cycle related to mesoscale convective systems (MCSs). The metric T skill scores increase significantly from the control simulation to the nudged simulations and from the nudged simulations with shorter wavelengths to the nudged simulations with longer wavelengths, but do not change much from MERRA-2 to the downscaled simulations or between the various downscaled simulations with different spatial resolutions. Thus, there is some credibility, but no significant value added compared to MERRA-2, of the downscaled climate simulations of the summer precipitation over the USGP.

Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as a hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each dataset and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged.

This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over central Brazil. These observed trends are found to be statistically significant using pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) as the baseline to estimate natural climate variability. The observed trends are compared with those obtained in natural-only CMIP5 simulations, in which only natural forcings (i.e. volcanoes and solar variability) are included, and in historical CMIP5 simulations, in which anthropogenic forcings (i.e. changes in the atmospheric composition) are further incorporated. The historical CMIP5 simulations are more successful in capturing the observed temperature trends than the simulations with natural forcings only. It is suggested that anthropogenic warming is already evident over much of South America. Unlike the warming trends, observed precipitation trends over South America are less spatially coherent with both negative and positive values across the continent. Significant positive trends are found over South America in only one of the data sets used, and over a region that roughly encompasses the southern part of La Plata Basin (southern Brazil, Uruguay, and northeastern Argentina) in all data sets used. The historical CMIP5 simulations do not capture this feature. No firm conclusions are reached, therefore, for anthropogenic influences on precipitation changes in the period selected for study.

The Multi-angle Imaging SpectroRadiometer (MISR) Joint Aerosol (JOINT_AS) Level 3 product has provided a global, descriptive summary of MISR Level 2 aerosol optical depth (AOD) and aerosol type information for each month over 16+ years since March 2000. Using Version 1 of JOINT_AS, which is based on the operational (Version 22) MISR Level 2 aerosol product, this study analyzes, for the first time, characteristics of observed and simulated distributions of AOD for three broad classes of aerosols: spherical nonabsorbing, spherical absorbing, and nonspherical – near or downwind of their major source regions. The statistical moments (means, standard deviations, and skewnesses) and distributions of AOD by components derived from the JOINT_AS are compared with results from two chemistry transport models (CTMs), the Goddard Chemistry Aerosol Radiation and Transport (GOCART) and SPectral RadIatioN-TrAnSport (SPRINTARS). Overall, the AOD distributions retrieved from MISR and modeled by GOCART and SPRINTARS agree with each other in a qualitative sense. Marginal distributions of AOD for each aerosol type in both MISR and models show considerable high positive skewness, which indicates the importance of including extreme AOD events when comparing satellite retrievals with models. The MISR JOINT_AS product will greatly facilitate comparisons between satellite observations and model simulations of aerosols by type.

According to long-term sea level reconstruction and steric sea level data, regional sea levels in the tropical Pacific have oscillated between east and west on a decadal time scale over the past 60 years, but the oscillation has been intensified significantly in the last three decades. Using conditional composite analysis, we show that the recent intensification in sea level variability is caused by modulation between the Pacific Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO), i.e., an El Nino in a positive PDO or a La Nina in a negative PDO phase. Our analysis of meteorological fields indicates that atmospheric circulation associated with the changes in ENSO-PDO phase relationship plays a positive role in enhancing the decadal sea level oscillation. The intensified sea level oscillation, when superimposed on the global trend of sea level rise, will have profound implications for coastal communities, therefore, the combined effect of PDO and ENSO should be taken into account in the decadal sea level prediction in the tropical Pacific.

The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Big data analytics traditionally involves download of massive amounts of datasets to common server/cluster for processing. Analytic process gets slower with increasing size of required data and network conditions. Data scientists also need explicit access to data locations to download required data. Explicit access to required data may not always be granted due to security reasons. To simplify and accelerate the analytics process on distributed big data with security considerations, we proposed the Virtual Information Fabric Infrastructure (VIFI) for data driven discoveries. Instead of moving large amounts of data to a common place of processing, VIFI allows automatic transfer of required analytics programs to the distributed data locations for in-place processing of relevant data. VIFI allows data scientists to conduct and coordinate complex analytics processes on distributed data repositories using containerization technology and open-source workflow design tools. VIFI alleviates users from having detailed knowledge of distributed data locations, as well as required dependencies, installation and configuration of analytical libraries. In this paper, we demonstrate our current and future work to improve the VIFI architecture using previous and additional uses cases, data management layer that simplifies search of relevant data sets through addition of metadata, integration with security policies at different institutions with the proposed VIFI security layer, and the use of a user-friendly web interface to carry different VIFI activities.

Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the seventeen RCM simulations are driven by boundary conditions from reanalysis while the other eleven are driven by one of four global climate models (GCMs). Four illustrative case studies are analyzed in detail. Model fidelity in LSMP spatial representation is high for cold winter extremes near Chicago. Winter warm extremes are captured by most RCMs in northern California, with some notable exceptions. Model fidelity is lower for cool summer days near Houston and extreme summer heat events in the Ohio Valley. Physical interpretation of these patterns and identification of well-simulated cases, such as for Chicago, boosts confidence in the ability of these models to simulate days in the tails of the temperature distribution. Results appear consistent with the expectation that the ability of an RCM to reproduce a realistically shaped frequency distribution for temperature, especially at the tails, is related to its fidelity in simulating LMSPs. Each ensemble member is ranked for its ability to reproduce LSMPs associated with observed warm and cold extremes, identifying systematically high performing RCMs and the GCMs that provide superior boundary forcing. The methodology developed here provides a framework for identifying regions where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts.

Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, the RCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes.

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Traditional data analytics involves manually identifying and downloading relevant distributed datasets of interest to a common server/cluster where the analytics processes are executed. For very large distributed datasets, this slows down the analytics process, and for extremely large datasets it is often impractical to download such massive volumes due to bandwidth limitations. In such cases, data scientists need to be provided explicit access to the remote servers hosting the datasets, and possess detailed knowledge of the server infrastructure and environments, in order to send their analytics packages to the data owner. This alternative poses considerable challenges and has not been adequately addressed to date. In this paper, we describe a novel approach to addressing this challenge called Virtual Information Fabric Infrastructure (VIFI) which seamlessly allows users to conduct analytics-in-place by distributing analytics to the distributed repositories without moving the underlying datasets to a common location. By allowing automated analytics scripts to be sent to the data and orchestration of distributed infrastructure, VIFI allows users to conduct, execute and coordinate complex analytics activities in-place with the data on multiple data repositories. VIFI uses Docker containerization technology along with open-source workflow tool NIFI to achieve automated orchestration and distributed analytics without requiring users to posses detailed knowledge of the distributed repositories and their underlying infrastructure. We demonstrate and evaluate VIFI on a Earth Science use-case for evaluation of precipitation over the Great Plains involving analytics on massive distributed data repositories.

This study evaluates model fidelity in simulating relationships between seasonally averaged precipitation, cloud fraction and surface insolation from the North American Regional Climate Change Assessment Project (NARCCAP) hindcast using observational data from ground stations and satellites. Model fidelity is measured in terms of the temporal correlation coefficients between these three variables and the similarity between the observed and simulated joint probability distribution functions (JPDFs) in 14 subregions over the conterminous United States. Observations exhibit strong negative correlations between precipitation/cloud fraction and surface insolation for all seasons, whereas the relationship between precipitation and cloud fraction varies according to regions and seasons. The skill in capturing these observed relationships varies widely among the NARCCAP regional climate models, especially in the Midwest and Southeast coast regions where observations show weak (or even negative) correlations between precipitation and cloud fraction in winter due to frequent non-precipitating stratiform clouds. Quantitative comparison of univariate and JPDFs indicates that model performance varies markedly between regions as well as seasons. This study also shows that comparison of JPDFs is useful for summarizing the performance of and highlighting problems with some models in simulating cloud fraction and surface insolation. Our quantitative metric may be useful in improving climate models by highlighting shortcomings in the formulations related with the physical processes involved in precipitation, clouds and radiation or other multivariate processes in the climate system.

Spatiotemporal characteristics of surface ozone (O3) variations over South Korea are investigated with consideration of meteorological factors and timescales based on the Kolmogorov–Zurbenko filter (KZ filter), using measurement data at 124 air quality monitoring sites and 72 weather stations for the 12 yr period of 1999–2010. In general, O3 levels at coastal cities are high due to dynamic effects of the sea breeze while those at the inland and Seoul Metropolitan Area (SMA) cities are low due to the NOx titration by local precursor emissions. We examine the meteorological influences on O3 using a combined analysis of the KZ filter and linear regressions between O3 and meteorological variables. We decomposed O3 time series at each site into short-term, seasonal, and long-term components by the KZ filter and regressed on meteorological variables. Impact of temperature on the O3 levels is significantly high in the highly populated SMA and inland region, but low in the coastal region. In particular, the probability of high O3 occurrence doubles with 4 °C of temperature increase in the SMA during high O3 months (May–October). This implies that those regions will experience frequent high O3 events in a future warming climate. In terms of short-term variation, the distribution of high O3 probability classified by wind direction shows the effect of both local precursor emissions and long-range transport from China. In terms of long-term variation, the O3 concentrations have increased by +0.26 ppbv yr−1 (parts per billion by volume) on nationwide average, but their trends show large spatial variability. Singular value decomposition analyses further reveal that the long-term temporal evolution of O3 is similar to that of nitrogen dioxide, although the spatial distribution of their trends is different. This study will be helpful as a reference for diagnostics and evaluation of regional- and local-scale O3 and climate simulations, and as a guide to appropriate O3 control policy in South Korea.

This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the Unite...

The associations between extreme temperature months and four prominent modes of recurrent climate variability are examined over South America. Associations are computed as the percent of extreme temperature months concurrent with the upper and lower quartiles of the El Nino–Southern Oscillation (ENSO), the Atlantic Nino, the Pacific Decadal Oscillation (PDO), and the Southern Annular Mode (SAM) index distributions, stratified by season. The relationship is strongest for ENSO, with nearly every extreme temperature month concurrent with the upper or lower quartiles of its distribution in portions of northwestern South America during some seasons. The likelihood of extreme warm temperatures is enhanced over parts of northern South America when the Atlantic Nino index is in the upper quartile, while cold extremes are often association with the lowest quartile. Concurrent precipitation anomalies may contribute to these relations. The PDO shows weak associations during December, January, and February, while in June, July, and August its relationship with extreme warm temperatures closely matches that of ENSO. This may be due to the positive relationship between the PDO and ENSO, rather than the PDO acting as an independent physical mechanism. Over Patagonia, the SAM is highly influential during spring and fall, with warm and cold extremes being associated with positive and negative phases of the SAM, respectively. Composites of sea level pressure anomalies for extreme temperature months over Patagonia suggest an important role of local synoptic scale weather variability in addition to a favorable SAM for the occurrence of these extremes.

[1] An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature.

The continuing increase in demand for commercial aviation transport raises questions about the effects of resulting emissions on the environment. The purpose of this study is to investigate, using a global chemistry transport model, to what extent aviation emissions outside the boundary layer influence air quality in the boundary layer. The large-scale effects of current levels of aircraft emissions were studied through comparison of multiple simulations allowing for the separated effects of aviation emissions occurring in the low, middle and upper troposphere. We show that emissions near cruise altitudes (9–11 km in altitude) rather than emissions during landing and take-off are responsible for most of the total odd-nitrogen (NOy), ozone (O3) and aerosol perturbations near the ground with a noticeable seasonal difference. Overall, the perturbations of these species are smaller than 1 ppb even in winter when the perturbations are greater than in summer. Based on the widely used air quality standards and uncertainty of state-of-the-art models, we conclude that aviation-induced perturbations have a negligible effect on air quality even in areas with heavy air traffic. Aviation emissions lead to a less than 1% aerosol enhancement in the boundary layer due to a slight increase in ammonium nitrate (NH4NO3) during cold seasons and a statistically insignificant aerosol perturbation in summer. In addition, statistical analysis using probability density functions, Hellinger distance, and p value indicate that aviation emissions outside the boundary layer do not affect the occurrence of extremely high aerosol concentrations in the boundary layer. An additional sensitivity simulation assuming the doubling of surface ammonia emissions demonstrates that the aviation induced aerosol increase near the ground is highly dependent on background ammonia concentrations whose current range of uncertainty is large.

The Multi-angle Imaging SpectroRadiometer (MISR) Joint Aerosol (JOINT_AS) Level 3 product provides a global, descriptive summary of MISR Level 2 aerosol optical depth (AOD) and aerosol type information for each month between March 2000 and the present. Using Version 1 of JOINT_AS, which is based on the operational (Version 22) MISR Level 2 aerosol product, this study analyzes, for the first time, characteristics of observed and simulated distributions of AOD for three broad classes of aerosols: non-absorbing, absorbing, and non-spherical – near or downwind of their major source regions. The statistical moments (means, standard deviations, and skewnesses) and distributions of AOD by components derived from the JOINT_AS are compared with results from the SPectral RadIatioN-TrAnSport (SPRINTARS) model, a chemistry transport model (CTM) with very high spatial and temporal resolution. Overall, the AOD distributions of combined MISR aerosol types show good agreement with those from SPRINTARS. Marginal distributions of AOD for each aerosol type in both MISR and SPRINTARS show considerable high positive skewness, which indicates the importance of including extreme AOD events when comparing satellite retrievals with models. The MISR JOINT_AS product will greatly facilitate comparisons between satellite observations and model simulations of aerosols by type.

In this paper, we describe an emergent tool called DAWN (short for "Distributed Analytics, Workflows and Numeric") which is a model for simulating, analyzing and optimizing system architectures for executing arbitrary data processing pipelines. As an example, we will apply DAWN to the investigation of a real-life Big Data use case in climate science: the evaluation of simulated rainfall characteristics using high-resolution observational data. We will show how DAWN can help in determining the optimal architecture, and science algorithms, to execute this case study analyzing distributed datasets, as a tradeoff between the overall time cost and the uncertainty of calculated metrics for model evaluation. We will also show how DAWN can guide architectural decisions for future research, specifically impacting how data should be generated and analyzed to cope with future projected data volumes.