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\begin{document}

\title{Understanding Active Layer Thickness Variability Under Changing Climatic
Conditions Across the North American Taiga-Tundra Ecotone}



\author[1]{Bradley Gay}%
\author[2]{Amanda Armstrong}%
\author[3]{Paul Montesano}%
\author[4]{Batuhan Osmanoglu}%
\author[5]{Kevin Schaefer}%
\author[4]{Kenneth Ranson}%
\author[6]{Howard Epstein}%
\affil[1]{George Mason University Fairfax}%
\affil[2]{Universities Space Research Association Columbia}%
\affil[3]{Science Systems and Applications, Inc.}%
\affil[4]{NASA Goddard Space Flight Center}%
\affil[5]{National Snow and Ice Data Center, Cooperative Institute for Research in the Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, U.S.A}%
\affil[6]{University of Virginia}%


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  \date{June 17, 2024}


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\begin{abstract}
In Alaska, pervasive irregularities of permafrost coverage and
associated boreal forest heterogeneity within the North American
Taiga-Tundra Ecological Transition Zone (TTE) are becoming more apparent
as the climate warms. These anomalies correspond to extensive shifts in
active layer thickness (ALT), carbon cycle disruption, and ecosystem
response patterns. The feedback complexities associated with these
climate-induced disturbances are evaluated with the integration of
remote sensing, modeling, field observations, data assimilation and
harmonization techniques, and artificial intelligence technology. In
this study, to improve our understanding of shifting belowground
dynamics and how they associate with aboveground vegetation patterns, we
used the SIBBORK-TTE model to derive permafrost degradation and
ecosystem transiency at high-resolution in this study. The
intercomparison of model version output was first examined; then,
multiple verification and validation methodologies revealed distinct
historical and future implications resulting from ALT variability within
four regions of the Alaska TTE domain (North Slope, Yukon Delta, Seward
Peninsula, Interior). To quantify historical thaw variability and
identify seasonality patterns across these regions of interest, in situ
ALT point measurements were collected from two campaigns (CALM, SMALT)
to cross-validate ALT-derived SAR data (AirMOSS, UAVSAR) and
below-ground SIBBORK-TTE simulations between 1990-2020. Future
conditions were then projected with a warming climate function and CMIP6
data from CNRM-CERFACS SSP126/585 scenarios. Initial results for derived
and measured annual maximum ALT yield a mean-error performance metric of
0.2294. Paradoxically, future climate conditions advance the ubiquity of
permafrost thaw and seasonality widening across the TTE. With this
investigative approach, spatiotemporal variability in ALT provides a
unique signal to enhance model precision and lower uncertainty through
fine-tuning driver forcing and modular parameterization, forecast
permafrost distribution, and identify the climatic and topographic
mechanisms of earth system feedbacks and land cover change.%
\end{abstract}%



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