Non-stationarity in extratropical north pacific atmospheric forcing of ENSO and its oscillatory behavior

As the largest mode of coupled climate variability, the El Niño Southern Oscillation (ENSO) carries consequences for weather patterns worldwide. In turn, communities that live in areas affected by ENSO variability would benefit from reliable and timely information on the occurrence of such events. To address this need, there has been an on-going effort within the scientific community to investigate and characterize the mechanisms that give rise to ENSO events. One of the greatest impediments to this effort, however, is that the ENSO system can behave both as a self-sustained, deterministic oscillation, and as a response to stochastic forcing. In this dissertation, we uncover a key determinant of these two types of ENSO behavior – namely North Pacific Oscillation (NPO)-induced variations in the northeasterly trade winds – and analyze how the variations in these trade winds influence ENSO variability historically and into the future. The first Chapter of this dissertation provides a thorough review of previous efforts to understand the initiation, onset, and evolution of ENSO events with a particular focus on the relationship between ENSO events and two NPO-related precursors, namely the Trade Wind Charging and the Northern Pacific Meridional Mode (TWC/NPMM). In Chapter 2 (Pivotti and Anderson 2021), we study the TWC/NPMM-ENSO relation over 140 years and uncover that there has been a multi-decadal modulation in the strength of TWC/NPMM’s influence upon ENSO events. Further, as a consequence of this modulation, ENSO behavior shifted between a stochastically forced interannual mode of variability to a multi-annual, quasi-regular one with a self-sustained oscillation and back again over the course of the last 140 years. In Chapter 3, we assess how the TWC/NPMM-ENSO relationship is reconstructed in state of the art coupled climate models. We find that across the ensemble the TWC/NPMM is reconstructed by all models as the leading ENSO precursor. Further, a subset of better-performing models confirms that during those periods when the TWC/NPMM-ENSO coupling is weak, ENSO oscillates regularly with constant periodicity, whereas when the coupling is strong, ENSO shows a more stochastic behavior. In Chapter 4, we introduce experiments with increasing anthropogenic radiative forcings from the same ensemble of coupled climate models evaluated in Chapter 3. We find that ENSO events initiated by TWC/NPMM are consistently stronger than standard ENSO events, independent of the presence/absence of increasing external forcings, however neither TWC/NPMM-induced ENSO events nor standard events show any systematic change in intensity in the presence of increasing atmospheric forcings. Further, there is no systematic change in TWC/NPMM-ENSO coupling in the presence of increasing external forcing and hence no systematic change in the oscillatory (or stochastic) behavior of the ENSO system. Finally, Chapter 5 of this dissertation includes a concluding summary and suggestions for future work. In summary, this dissertation shows for the first time that the ENSO system can behave both as a self-sustained oscillation and as a response to stochastic forcing, that the modulation of this behavior is linked to the strength of TWC/NPMM-ENSO coupling, and that the strength of this coupling is the result of multi-decadal internal climate variability and not human-induced climate change

Moth and Birch

The goal of this thesis is to build a near-real time defoliation detector that could be used, early in the spring, to find out which areas of a birch forest are being affected by an insect outbreak. The importance of a reliable detection at the beginning of the spring lies on the possibility for an early intervention. The data on which the study is based is the Normalized Difference Vegetation Index (NDVI), which is calculated from the 8-days interval measurements, obtained from the MODerate resolution Imaging Spectroradiometer (MODIS). Further available information that is included in the analysis are the forest fraction and the altitude of each pixel. The first step of the analysis is fitting a function to the measurements of each pixel whose parameters could capture the important aspects of the changes in NDVI values. Among them, the final NDVI value that is reached during the summer is of particular interest since an abnormally low value could be the indication of an insect infestation. Different assumptions are made on the error distribution. The first ones, more simplystic, do not manage to counteract the noise. A more complex error structure is thus taken into account, leading to a better estimate which is then used to build the estimator. The idea behind the detector is to identify those pixels for which the NDVI does not reach its high late spring/summer values fast enough, with respect to other pixels and previous years. It is known that the two years, among those available in the dataset, that have suffered a moth outbreak are 2004 and 2013. Hence, the estimation of the fitting function is run for 2000-2003, the detector is tried on 2004 and finally tested on 2013, since for this year a few locations of the outbreak were known. The discrepancy between field data and the results generated by the detector suggests further adjustments that would improve the capacity to detect moth infestation.Early and reliable detection of pest infestation in forests is crucial to protect the health of trees. Insect outbreaks are an important cause of defoliation: they delay the blossoming of leaves and thereby affect the growth of trees. Such delays lower the economic potential of forests as well as their capacity to absorb atmospheric CO 2 . The aim of this thesis is to use mathematical models to build a near real-time detector that can identify abnormal behavior in forest growth, which can be the sign of pest infestation. In order for the detector to provide effective early warnings, the analysis uses high time-resolution satellite images. In particular, the detector is based on data collected at the Abisko National Park in the north of Sweden. The insect that is known to threaten this birch forest is a moth called Epirrita Autumnata, whose larvae feed on the bursting leaves early in the spring. The data used to study the behaviour of green mass is the Normalized Difference Vegetation Index (NDVI). A dataset of 14 years of measurements was available over an area of 350 km 2 . The index is calculated based on images collected by the MODerate resolution Imaging Spectroradiometer (MODIS), which is placed on two satellites that orbit Earth and collects images of the surface daily. The analysis also includes information on forest fraction and altitude for each pixel of the area. The first step of the analysis is fitting a function to the NDVI measurements of each pixel. The chosen function captures all important aspects of the change in NDVI during the spring. Different methods are used to fit the function to the NDVI. The first, simplistic models fail to fit the fuction because of the strong noise that affects the measurements. Therefore more robust and complex estimators are tried out. The final, best performing technique is used to construct the detector. The idea behind the detector is to identify those pixels for which NDVI grows slower than expected, based on the values of other pixels and previous years, during the spring. In particular, it is known that, within the available dataset, the two years that have suffered a moth outbreak are 2004 and 2013. Hence, the chosen function is fitted to the data from 2000-2003, in order to get a sense of the behaviour of NDVI during ”healthy” years. The detection of abnormal behaviour is done for 2004 and it is then tested on 2013. For this last year, a few locations of the outbreak were known, so that the results generated by the detector could be verified. The discrepancy between field data and the results generated by the detector suggests further adjustments that would improve the capacity to detect moth infestation

Assessing the future influence of the North Pacific trade wind precursors on ENSO in the CMIP6 HighResMIP multimodel ensemble

The El Nino Southern Oscillation (ENSO), as one of the largest coupled climate modes, influences the livelihoods of millions of people and ecosystems survival. Thus, how ENSO is expected to behave under the influence of anthropogenic climate change is a substantial question to investigate. In this paper, we analyze future predictions of specific traits of ENSO, in combination with a subset of well-established precursors-the Trade Wind Charging and North Pacific Meridional Mode (TWC/NPMM). We study it across three sets of experiments from a protocol-driven ensemble from CMIP6-the High Resolution Model Intercomparison Project (HighResMIP). Namely, (1) experiments at constant 1950's radiative forcings, and (2) experiments of present (1950-2014) and (3) future (2015-2050) climate with prescribed increasing radiative forcings. We first investigate the current and predicted spatial characteristics of ENSO events, by calculating area, amplitude and longitude of the Center of Heat Index (CHI). We see that TWC/NPMM-charged events are consistently stronger, in both the presence and absence of external forcings; however, as anthropogenic forcings increase, the area of all ENSO events increases. Since the TWC/NPMM-ENSO relationship has been shown to affect the oscillatory behavior of ENSO, we analyze ENSO frequency by calculating CHI-analogous indicators on the Continuous Wavelet Transform (CWT) of its signal. With this new methodology, we show that across the ensemble, ENSO oscillates at different frequencies, and its oscillatory behavior shows different degrees of stochasticity, over time and across models. However, we see no consistent indication of future trends in the oscillatory behavior of ENSO and the TWC/NPMM-ENSO relationship

Topology based identification and comprehensive classification of four-transmembrane helix containing proteins (4TMs) in the human genome

Background: Membrane proteins are key components in a large spectrum of diverse functions and thus account for the major proportion of the drug-targeted portion of the genome. From a structural perspective, the a-helical transmembrane proteins can be categorized into major groups based on the number of transmembrane helices and these groups are often associated with specific functions. When compared to the well-characterized seven-transmembrane containing proteins (7TM), other TM groups are less explored and in particular the 4TM group. In this study, we identify the complete 4TM complement from the latest release of the human genome and assess the 4TM structure group as a whole. We functionally characterize this dataset and evaluate the resulting groups and ubiquitous functions, and furthermore describe disease and drug target involvement. Results: We classified 373 proteins, which represents similar to 7 % of the human membrane proteome, and includes 69 more proteins than our previous estimate. We have characterized the 4TM dataset based on functional, structural, and/or evolutionary similarities. Proteins that are involved in transport activity constitute 37 % of the dataset, 23 % are receptor-related, and 13 % have enzymatic functions. Intriguingly, proteins involved in transport are more than double the 15 % of transporters in the entire human membrane proteome, which might suggest that the 4TM topological architecture is more favored for transporting molecules over other functions. Moreover, we found an interesting exception to the ubiquitous intracellular N- and C-termini localization that is found throughout the entire membrane proteome and 4TM dataset in the neurotransmitter gated ion channel families. Overall, we estimate that 58 % of the dataset has a known association to disease conditions with 19 % of the genes possibly involved in different types of cancer. Conclusions: We provide here the most robust and updated classification of the 4TM complement of the human genome as a platform to further understand the characteristics of 4TM functions and to explore pharmacological opportunities

Additional file 1: of Topology based identification and comprehensive classification of four-transmembrane helix containing proteins (4TMs) in the human genome

Contains the UniProt identifications, gene symbols, and Ensembl protein identifications for the classification of the 494 valid 4TM dataset. Also included within the table is the following information: signal peptides, topology, Pfam domains, functional classifications, review status, CCDS identifier, and gene-disease associations. (XLSX 176 kb