Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids

Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

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, theRCMs 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

Putting into action the REGCM4.6 regional climate model for the study of climate change, variability and modeling over Central America and Mexico

What: International experts and attendees from several countries of Central America, Mexico, the Caribbean (CAM), and South America (SA) met to discuss regional issues on climate variability and climate change to learn the use of the non-hydrostatic version of the International Center for Theoretical Physics (ICTP) RegCM4.6 model, and to establish a regional modeling scientific community for understanding the physics of climate processes and the generation of regional climate change scenarios. When: 14-18 November 2016. Where: Center for Geophysical Research (CIGEFI in Spanish) and School of Physics, University of Costa Rica (UCR), San José, Costa Rica.Ministerio de Ciencia, Tecnología y Telecomunicaciones/[FI-0015-16]/MICITT/Costa RicaUniversidad de Costa Rica/[805-B0-065]/UCR/Costa RicaUniversidad de Costa Rica/[805-A8-606]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-130]/UCR/Costa RicaUniversidad de Costa Rica/[805-A9-224]/UCR/Costa RicaUniversidad de Costa Rica/[805-A7-002]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-402]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-600]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-227]/UCR/Costa RicaUniversidad de Costa Rica/[805-B5-296]/UCR/Costa RicaUniversidad de Costa Rica/[808-A9-180]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Study on impacts of aviation emissions and related dynamics and chemistry

The continuing increase in demand for commercial aviation transport raises questions about the effects of resulting emissions on the environment. The upper troposphere and lower stratosphere (UTLS), where most of aviation emissions occur, plays an important role in climate and atmospheric chemistry. The main purpose of this study is to investigate, using chemistry transport models, how the aviation emissions influence radiative forcing in the UTLS and air quality in the boundary layer. Using the MOZART-3.1 chemistry-transport model of the global troposphere and stratosphere, the relative impacts of emissions from the ground and ocean transportation sector to aviation emissions on the atmospheric composition near the tropopause were investigated. The separate contributions of nitrogen oxides (NOx), carbon monoxide (CO) emissions and the combined effects of NOx and hydrocarbons (HC) from surface traffic emissions to UTLS ozone (O3) were considered. The analyses suggest that O3 in the upper troposphere and lower stratosphere is affected more by surface traffic emissions than aviation emissions. The role of peroxyacetylnitrate (PAN) and the quasi-horizontal transport in the UTLS is important for aviation impacts and ground-based HC effect on O3 in the UTLS. Despite its importance, the quasi-horizontal transport process in the UTLS, represented by global chemistry-transport models (CTMs) or chemistry-climate models (CCMs), cannot easily be diagnosed with conventional analyses on isobaric surfaces. So this study suggests some diagnostic tools to better evaluate CTMs and CCMs relative to satellite observations in the region of UTLS. Using the Hellinger distance, vertical profiles of probability density functions (PDFs) of chemical tracers simulated by the MOZART-3.1 are quantitatively compared with satellite data from the Microwave Limb Sounder (MLS) instrument in the tropopause relative altitude to characterize features of tracer distributions near the tropopause. Overall, the comparison of PDFs between MLS and MOZART-3.1 did not satisfy the same population assumption. Conditional PDFs are used to understand the meteorological differences between global climate models and the real atmosphere and the conditional PDFs between MOZART-3.1 and MLS showed better agreement compared to the original PDFs. The low static stability during high tropopause heights at midlatitudes suggests that the variation of tropopause height is related to transport processes from the tropics to midlatitudes. MOZART-3.1 reproduces episodes of tropical air intrusions that are similar to observed. However, some diagnostic analyses show that MOZART-3.1 and CCMs in general need some improvements for better simulating the UTLS especially when the tropopause at midlatitudes is high. We also find that the aviation emissions near cruise altitudes are responsible for most of the small boundary layer perturbations in concentrations of total odd-nitrogen (NOy), O3 and aerosols. Overall the aviation induced perturbations are too small to be important even in areas with heavy air traffic. The small perturbations of NOy, O3 and aerosols show seasonal differences caused by different concentrations of background aerosols and related heterogeneous reactions in the middle troposphere. The effects of aviation emissions on the boundary layer perturbations are stronger in the winter compared to summer for the same amount of emissions. However, the stronger perturbation in winter, especially O3 increase, is not important for air quality. In addition, aircraft emit NOx but aircraft emissions near cruise altitudes actually decrease NOx in urban areas of the Northern Hemisphere in winter. Heterogeneous reactions and nitrate radical (NO3) play an important role to reduce the background NOx so they also limit the O3 increase near the ground in winter. Aviation emissions lead to less than 1% of aerosol enhancement in the boundary layer by slightly increasing ammonium nitrate during cold seasons. However, despite some statistically significant aerosol perturbations at some grid points, the upper tropospheric aviation emissions do not increase the occurrence of extreme aerosol concentrations in the boundary layer, with likely little effect on human health. A sensitivity study with doubled ground ammonia flux shows the high dependence of aviation induced aerosol increase near the ground on background ammonia. This indicates that the aerosol perturbations resulting from aviation emissions are within models’ uncertainty range

AOD_monthly_2000-MAR_2017-FEB_from_MISR_L3_JOINT.nc

The monthly mean AOD for total aerosols (hereafter referred to as total AOD), non-absorbing aerosols (non-absorbing AOD), absorbing aerosols (absorbing AOD), and non-spherical aerosols (non-spherical AOD) for the wavelength of 558 $nm$ over the 17-year period from March 2000 through February 2017. The monthly mean AODS were calculated using the Multi-angle Imaging SpectroRadiometer (MISR)'s Level 3 Joint Aerosol Product

AOD_monthly_2000-MAR_2016-FEB_from_MISR_L3_JOINT.nc

Monthly mean and standard deviation of optical depths for three different types of atmospheric aerosols. The original data is from Multiangle Imaging SpectroRadiometer Level 3 Joint aerosol product.<br

Classifying Reanalysis Surface Temperature Probability Density Functions (PDFs) over North America with Cluster Analysis

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

Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades?

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

Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids

Evaluation Of Cmip5 Ability To Reproduce Twentieth Century Regional Trends In Surface Air Temperature And Precipitation Over Conus

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