Sea Level Hazards: Altimetric Monitoring of Tsunamis and Sea Level Rise

Whether on the short timescale of an impending tsunami or the much longer timescale of climate change-driven sea level rise, the threat stemming from rising and inundating ocean waters is a great concern to coastal populations. Timely and accurate observations of potentially dangerous changes in sea level are vital in determining the precautionary steps that need to be taken in order to protect coastal communities. While instruments from the past have provided in situ measurements of sea level at specific locations across the globe, satellites can be used to provide improved spatial and temporal sampling of the ocean in addition to producing more accurate measurements. Since 1993, satellite altimetry has provided accurate measurements of sea surface height (SSH) with near-global coverage. Not only have these measurements led to the first definitive estimates of global mean sea level rise, satellite altimetry observations have also been used to detect tsunami waves in the open ocean where wave amplitudes are relatively small, a vital step in providing early warning to those potentially affected by the impending tsunami. The use of satellite altimetry to monitor two specific sea level hazards is examined in this thesis. The first section will focus on the detection of tsunamis in the open ocean for the purpose of providing early warning to coastal inhabitants. The second section will focus on estimating secular trends using satellite altimetry data with the hope of improving our understanding of future sea level change. Results presented here will show the utility of satellite altimetry for sea level monitoring and will lay the foundation for further advancement in the detection of the two sea level hazards considered

Effects of Climate Oscillations on Wildland Fire Potential in the Continental United States

The effects of climate oscillations on spatial and temporal variations in wildland fire potential in the continental U.S. are examined from 1979 to 2015 using cyclostationary empirical orthogonal functions (CSEOFs). The CSEOF analysis isolates effects associated with the modulated annual cycle and the El Nino-Southern Oscillation (ENSO). The results show that, in early summer, wildland fire potential is reduced in the southwest during El Nino but is increased in the northwest, with opposite trends for La Nina. In late summer, El Nino is associated with increased wildland fire potential in the southwest. Relative to the mean, the largest impacts of ENSO are observed in the northwest and southeast. Climate impacts on fire potential due to ENSO are found to be most closely associated with variations in relative humidity. The connections established here between fire potential and climate oscillations could result in improved wildland fire risk assessment and resource allocation

Theoretical Foundation of Cyclostationary EOF Analysis for Geophysical and Climatic Variables: Concepts and Examples

Natural variability is an essential component of observations of all geophysical and climate variables. In principal component analysis (PCA), also called empirical orthogonal function (EOF) analysis, a set of orthogonal eigenfunctions is found from a spatial covariance function. These empirical basis functions often lend useful insights into physical processes in the data and serve as a useful tool for developing statistical methods. The underlying assumption in PCA is the stationarity of the data analyzed; that is, the covariance function does not depend on the origin of time. The stationarity assumption is often not justifiable for geophysical and climate variables even after removing such cyclic components as the diurnal cycle or the annual cycle. As a result, physical and statistical inferences based on EOFs can be misleading. Some geophysical and climatic variables exhibit periodically time-dependent covariance statistics. Such a dataset is said to be periodically correlated or cyclostationary. A proper recognition of the time-dependent response characteristics is vital in accurately extracting physically meaningful modes and their space-time evolutions from data. This also has important implications in finding physically consistent evolutions and teleconnection patterns and in spectral analysis of variability-important goals in many climate and geophysical studies. In this study, the conceptual foundation of cyclostationary EOF (CSEOF) analysis is examined as an alternative to regular EOF analysis or other eigenanalysis techniques based on the stationarity assumption. Comparative examples and illustrations are given to elucidate the conceptual difference between the CSEOF technique and other techniques and the entailing ramification in physical and statistical inferences based on computational eigenfunctions. © The authors 2015

Sea Level Acceleration in the China Seas

While global mean sea level rise (SLR) and acceleration (SLA) are indicators of climate change and are informative regarding the current state of the climate, assessments of regional and local SLR are essential for policy makers responding to, and preparing for, changes in sea level. In this work, three acceleration detection techniques are used to demonstrate the robust SLA in the China Seas. Interannual to multidecadal sea level variations (periods \u3e2 years), which are mainly related to natural internal climate variability and significantly affect estimation of sea level acceleration, are removed with empirical mode decomposition (EMD) analysis prior to the acceleration determination. Consistent SLAs of 0.085 ± 0.020 mm·yr−2 (1950–2013) and 0.074 ± 0.032 mm·yr−2 (1959–2013) in regional tide gauge records are shown to result from the three applied approaches in the Bohai Sea (BS) and East China Sea (ECS), respectively. The SLAs can be detected in records as short as 20 years if long-term sea level variability is adequately removed. The spatial distribution of SLA derived from a sea level reconstruction shows significant SLA in the BS, Yellow Sea (YS) and Yangtze River Estuary

Global Sea-Level Budget 1993-Present

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g.,acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled Regional Sea Level and Coastal Impacts , an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 ± 0.3mm yr(-1) and acceleration of 0.1 mm yr(-2) over 1993-present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present period. We also study the sea-level budget over 2005-present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3 mm yr(-1) (1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level

Reconstruction of Sea Level Around the Korean Peninsula Using Cyclostationary Empirical Orthogonal Functions

Since the advent of the modern satellite altimeter era, the understanding of the sea level has increased dramatically. The satellite altimeter record, however, dates back only to the 1990s. The tide gauge record, on the other hand, extends through the 20th century but with poor spatial coverage when compared to the satellites. Many studies have been conducted to create a dataset with the spatial coverage of the satellite datasets and the temporal length of the tide gauge records by finding novel ways to combine the satellite data and tide gauge data in what is known as sea level reconstruction. However, most of the reconstructions of sea level were conducted on a global scale, leading to reduced accuracy on regional levels, especially when there are relatively few tide gauges. The seas around the Korean Peninsula are one such area with few tide gauges before 1960. In this study, new methods are proposed to reconstruct past sea level around the Korean Peninsula. Using spatial patterns obtained from a cyclostationary empirical orthogonal function decomposition of satellite data, we reconstruct sea level over the period from 1900 to 2014. Sea surface temperature data and altimeter data are used simultaneously in the reconstruction process, leading to an elimination of reliance on tide gauge data. Although we did not use the tide gauge data in the reconstruction process, the reconstructed sea level has a better agreement with the tide gauge observations in the region than previous studies that incorporated the tide gauge data. This study demonstrates a reconstruction technique that can potentially be used at regional levels, with particular emphasis on areas with poor tide gauge coverage

Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

Sea ice loss is proposed as a primary reason for the Arctic amplification, although the physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-Interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice loss in the Arctic Ocean and the Arctic amplification. While sea ice loss is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains thin in winter only in the Barents-Kara seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice reduction warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be free of ice. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara seas and Laptev, East Siberian, Chukchi, and Beaufort seas

20th Century Multivariate Indian Ocean Regional Sea Level Reconstruction

Despite having some of the world\u27s most densely populated and vulnerable coastlines, Indian Ocean sea level variability over the past century is poorly understood relative to other ocean basins primarily, due to the short and sparse observational records. In an attempt to overcome the limitations imposed by the lack of adequate observations, we have produced a 20th century Indian Ocean sea level reconstruction product using a new multivariate reconstruction technique. This technique uses sea level pressure and sea surface temperature in addition to sea level data to help constrain basin‐wide sea level variability by (1) the removal of large spurious signals caused as a result of insufficient tide gauge data specifically during the first half of the 20th century and (2) through its information on large‐scale climate modes such as El Niño‐Southern Oscillation and Indian Ocean Dipole. Basis functions generated by Cyclostationary Empirical Orthogonal Functions are used for the reconstruction. This new multivariate technique provides improved regional sea level variability estimates along with a longer record length in comparison to existing globally reconstructed sea level data. The biggest advantage of using this multivariate reconstruction technique lies in its ability to reconstruct Indian Ocean sea level for the first half of the 20th century, providing a long sea level record for the study of Indian Ocean internal climate variability. This will enable future studies to help improve the understanding of how sea level trends and variability can be modulated by internal climate variability in the Indian Ocean

An Assessment of Regional ICESat-2 Sea-Level Trends

Sea-level rise is an important indicator of ongoing climate change and well observed by satellite altimetry. However, observations from conventional altimetry degrade at the coast where regional sea-level changes can deviate from the open-ocean and impact local communities. With the 2018 launch of the laser altimeter onboard ICESat-2, new high-resolution observations of ice, land, and ocean elevations are available. Here we assess the potential benefits of sea level measured by ICESat-2 by comparing to data from Jason-3 and tide gauges. We find good agreement in the linear rates computed from the independent observations, with an absolute average residual of 3.60 ± 0.03 cm yr−1 between global ICESat-2 and Jason-3 observations at a 1° posting. The recent La Niña is clearly evident in ICESat-2 observations, as well as small-scale features. By demonstrating the quality of the ICESat-2-measured sea level, we provide support for integrating it into the existing suite of sea-level observations