\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Quantifying Feedback Sensitivities of Permafrost Degradation and Carbon
Release with Earth Observation Data and Feedback Neural Networks}



\author[1]{Bradley Gay}%
\author[1]{Amanda H. Armstrong}%
\author[1]{Neal J. Pastick}%
\author[1]{Hélène Genet}%
\author[1]{Jennifer D. Watts}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
It is well-established that positive feedbacks between permafrost
degradation and the release of soil carbon into the atmosphere impact
land-atmosphere feedback mechanisms, disrupt the global carbon cycle,
and accelerate climate change. Permafrost dynamics are relevant to the
global community because the distribution of this frozen ground
substrate characterizes nearly 23 million square kilometers of the
northern latitudes. The widespread distribution of thawing permafrost is
causing a cascade of geophysical and biochemical disturbances with
global impact. Current earth system models do not account for permafrost
carbon feedback mechanisms; we are exploring, simulating, and
quantifying this limitation with field-scale surveys and numerical
modeling, image processing, and machine learning at scale across the
tundra and taiga ecosystems (TTE). This research seeks to identify,
interpret, and explain the causal links and feedback sensitivities
attributed to permafrost degradation and terrestrial carbon cycling
asymmetry with in situ observations, remote sensing imagery, modeling
and reanalysis products, and a hybridized multimodal deep learning
ensemble of recurrent, convolutionally-layered, memory-based networks
(\href{https://tinyurl.com/z25u5n9d}{GeoCryoAI}). Preliminary metrics
obtained from mirroring freeze-thaw dynamics and soil carbon flux across
four subdomain in Alaska yield a root mean square error of 6.3637 and
4.7973, respectively. More specifically, this data-driven modeling
ensemble is composed of a convolutional neural network-filtered (CNN)
long short-term memory-encoded (LSTM) recurrent neural network that
integrates teacher learning from in situ observations while embedding
satellite-based measurements and time series datasets into a network of
activation functions and processing layers. These outputs are then
trained within a variational autoencoder framework (VAE) that encodes
and imputes proper decoding protocol necessary for generative
adversarial training, benchmarking, and reconstructing synthetic time
series data for gap-filling and feature learning. Ongoing work
demonstrates the fidelity of monitoring active layer thickness (ALT)
variability as a sensitive, silent-but-pronounced harbinger of change; a
unique signal for characterizing and forecasting permafrost degradation,
soil carbon flux, and other biogeochemical drivers facilitating land
cover change and earth system feedbacks. These multimodal approaches to
knowledge discovery will not only improve sensitivity analyses and
disentangle the spatial processes and causal links behind drivers of
change, but also reconcile disparate estimations and below-ground
uncertainty across the Arctic system.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

