A time-varying causality formalism based on the Liang-Kleeman information flow for analyzing directed interactions in nonstationary climate systems

Daniel Fiifi Tawia Hagan, Guojie Wang, X. San Liang, Han A.J. Dolman

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang-Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its timeinvariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture-air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

Original languageEnglish
Pages (from-to)7521-7537
Number of pages17
JournalJournal of Climate
Volume32
Issue number21
Early online date7 Oct 2019
DOIs
Publication statusPublished - 1 Nov 2019

Fingerprint

land surface
climate
air temperature
soil moisture
time series
atmosphere
Kalman filter
wavelet
asymmetry
humidity
transform
drought
energy
water
heat wave

Cite this

@article{fc69794dd1e940a2be40bdc1f31a8a61,
title = "A time-varying causality formalism based on the Liang-Kleeman information flow for analyzing directed interactions in nonstationary climate systems",
abstract = "The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang-Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its timeinvariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture-air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.",
author = "Hagan, {Daniel Fiifi Tawia} and Guojie Wang and Liang, {X. San} and Dolman, {Han A.J.}",
year = "2019",
month = "11",
day = "1",
doi = "10.1175/JCLI-D-18-0881.1",
language = "English",
volume = "32",
pages = "7521--7537",
journal = "Journal of Climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "21",

}

A time-varying causality formalism based on the Liang-Kleeman information flow for analyzing directed interactions in nonstationary climate systems. / Hagan, Daniel Fiifi Tawia; Wang, Guojie; Liang, X. San; Dolman, Han A.J.

In: Journal of Climate, Vol. 32, No. 21, 01.11.2019, p. 7521-7537.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - A time-varying causality formalism based on the Liang-Kleeman information flow for analyzing directed interactions in nonstationary climate systems

AU - Hagan, Daniel Fiifi Tawia

AU - Wang, Guojie

AU - Liang, X. San

AU - Dolman, Han A.J.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang-Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its timeinvariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture-air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

AB - The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang-Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its timeinvariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture-air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

UR - http://www.scopus.com/inward/record.url?scp=85074945261&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074945261&partnerID=8YFLogxK

U2 - 10.1175/JCLI-D-18-0881.1

DO - 10.1175/JCLI-D-18-0881.1

M3 - Article

VL - 32

SP - 7521

EP - 7537

JO - Journal of Climate

JF - Journal of Climate

SN - 0894-8755

IS - 21

ER -