title: Cyber security vulnerabilities in Tonga

authors: Laulaupea'alu, Siuta

subjects: computer science

abstract: A research project undertaken in Tonga in 2016 showed that Tonga's computer systems are susceptible and can be easily exploited by cybercriminals. This paper builds on that research by identifying areas of vulnerability and then recommending how these vulnerabilities can be addressed. Areas discussed include penetration testing, a data recovery plan, budget considerations, cyber insurance, the employment of cyber security experts, information security policies, data encryption, the backing up of data, two factor authentication, the integrity of passwords, and database considerations. By detailing and discussing these issues it is hoped that an awareness can be raised by organisations and government departments of Tonga and that the people of Tonga can best prepare themselves for the increase in cybercrime and cyberattacks that is likely to occur in the Pacific.

text: Cyber security vulnerabilities in Tonga A research project undertaken in Tonga in 2016 showed that Tonga's computer systems are susceptible and can be easily exploited by cybercriminals. This paper builds on that research by identifying areas of vulnerability and then recommending how these vulnerabilities can be addressed. Areas discussed include penetration testing, a data recovery plan, budget considerations, cyber insurance, the employment of cyber security experts, information security policies, data encryption, the backing up of data, two factor authentication, the integrity of passwords, and database considerations. By detailing and discussing these issues it is hoped that an awareness can be raised by organisations and government departments of Tonga and that the people of Tonga can best prepare themselves for the increase in cybercrime and cyberattacks that is likely to occur in the Pacific.

year: 2019

title: A detailed characterisation of the wave climate around New Zealand and its variability

authors: Godoi, Victor Azevedo

subjects: wave climate

abstract: Ocean waves can cause severe social and economic impacts. Therefore, understanding their behaviour is of paramount importance for the effective management of coastal and ocean hazards. This thesis thoroughly investigates four aspects (described below) of the wave climate around New Zealand and its variability by using 44 years (1958–2001) of wave hindcast data. These data were provided by the National Institute of Water and Atmospheric Research Ltd, and were produced using the WAVEWATCH III model forced with wind and ice fields from the ERA-40 reanalysis project.
Relationships between mean wave parameters (significant wave height (Hs), peak and mean wave periods, and peak wave direction) and several climate patterns were analysed. Climate indices representative of the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Zonal Wavenumber-3 Pattern (ZW3), and Southern Annular Mode (SAM) were correlated with the mean wave parameters using the Pearson’s correlation coefficient and the wavelet spectral analysis. Moreover, mean annual and inter-annual variabilities and trends in Hₛ were computed for the 44-year period. In general, larger annual and inter-annual variabilities were found along the coastline, in regions dominated by local winds. An increasing trend in Hₛ was found around the country, with values varying between 1 and 6 cm/decade at the shoreline. The largest trends in Hₛ were detected to the south of 48°S, suggesting a relationship with the trend toward a positive SAM. The wave parameters showed a strong connection with seasonal to decadal variabilities in the SAM throughout the period analysed. In addition, larger waves were observed during extreme ENSO and IOD events at inter-annual timescales, while they were more evident at intra-seasonal and seasonal timescales in the correlations with the ZW3. Negative phases of the ZW3 and ENSO and positive phases of the IOD, PDO, and SAM were associated with larger waves around most parts of New Zealand.
A detailed climatology of extreme wave events for New Zealand waters was also established, and estimates of Hₛ for up to 100-year return periods were calculated. Although comparisons to buoy data at three locations around New Zealand showed negative biases in the hindcast data, the latter still provided a suitable basis for trend, spatial distribution, and frequency analyses. Results indicate some similarities to patterns previously shown in the mean wave climate, with the largest waves found in southern New Zealand, and the smallest ones observed in areas sheltered from southwesterly swells. The number of extreme events varied substantially throughout the year for the period 1958–2001, while their intensity was more consistent. Extreme events occurred more/less frequently in winter/summer months. The greatest mean annual variability of extreme Hₛ was found on the north coasts of both the North and South Islands, where more locally-generated storms drive the extremes. The inter-annual variability was largest along the north coast of the country and on the east coast of the South Island, suggesting relationships with La Niña-like effects and the SAM, respectively. Furthermore, the known trend for a more positive SAM may explain the increasing number of extreme events on the south and east coasts observed in trend analysis.
Clusters of storm waves contribute disproportionately to coastal erosion hazards because the coastline has insufficient time to recover between events. The change in occurrence of clustered storms and its association with atmospheric oscillation modes were also investigated in New Zealand waters. In order to do so, long-term averages of cluster parameters (number of storms within the cluster, potential for coastal erosion, and cluster duration) were firstly assessed. Then, the relationships between clustering and the ENSO, IOD, ZW3, PDO, and SAM were explored through correlation analysis over several timescales. Clusters were more frequently observed to the northeast of New Zealand and on the central eastern coast of the South Island. The most vulnerable regions to cluster-induced coastal erosion were southern New Zealand and the northwestern coast, which resulted from steady southwesterly swells, although clusters with the longest duration occurred on the east coast of the South Island. Trends suggest that clusters have incorporated more storms, have become more hazardous, and have increased in duration, particularly along the South Island coastline. Although these trends may be sensitive to the reanalysed wind fields used to force the wave hindcast, they reflect trends in the ENSO, PDO, and SAM. Stronger southwesterly winds during El Niño (negative ENSO) and El Niño-like conditions (positive IOD/PDO) generated more clustered storms mainly on the southwestern coast of New Zealand, whereas increases in clustering were observed on the north coast during La Niña and La Niña-like conditions (stronger northeasterly winds). Higher occurrence of clustering was also evident on the west coast during the strong atmospheric zonal flow associated with negative ZW3. Lastly, strengthened westerlies related to positive SAM led to increased clustering primarily to the south of New Zealand.
The last aspect of the wave climate around New Zealand explored in this thesis was the modulation of Hₛ variability by wind anomalies associated with the co-occurrence of the Madden-Julian Oscillation (MJO) and ENSO. For this purpose, Hₛ and wind anomalies composites were created using 23 years (1979–2002) of the wave hindcast data and ERA-40 winds. Composites were calculated for November–March periods, when simultaneous ENSO-MJO phase pairs are potentially most active. Results showed striking features: El Niño-related wave conditions (which consist of increased Hₛ along the west and south coasts of New Zealand) are reinforced during MJO phase 8, whereas the wave conditions associated with La Niña (which consist of larger Hₛ along the north coast) are enhanced during MJO phase 6; Similar wave anomalies are generated during opposing ENSO phases (La Niña and El Niño) when these are combined with MJO phases 3 and 5, respectively; The majority of statistically significant Hₛ anomalies disappear from the study area
during El Niño-MJO phase 6 and La Niña-MJO phase 4, showing the neutralising nature of some phase combinations; Lastly, negative Hₛ anomalies are experienced during El Niño-MJO phase 4, in contrast to the positive anomalies expected during El Niño events. These results clearly show the importance of remote forcing to wave anomalies in the New Zealand region and highlight the need to assess atmospheric and oceanic conditions considering multiple climate oscillations.
This thesis has shown that the wave climate around New Zealand is affected by a range of atmospheric conditions, which have significantly different impacts along the coastline. All these conditions should be taken into account in order to mitigate future hazards. Therefore, the results presented here may assist coastal communities and stakeholders as well as offshore activities around the country in better prepare for potential impacts. Additionally, these results contribute to enhancing the research community knowledge of wave climatology in an area with recognised

text: A detailed characterisation of the wave climate around New Zealand and its variability Ocean waves can cause severe social and economic impacts. Therefore, understanding their behaviour is of paramount importance for the effective management of coastal and ocean hazards. This thesis thoroughly investigates four aspects (described below) of the wave climate around New Zealand and its variability by using 44 years (1958–2001) of wave hindcast data. These data were provided by the National Institute of Water and Atmospheric Research Ltd, and were produced using the WAVEWATCH III model forced with wind and ice fields from the ERA-40 reanalysis project.
Relationships between mean wave parameters (significant wave height (Hs), peak and mean wave periods, and peak wave direction) and several climate patterns were analysed. Climate indices representative of the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Zonal Wavenumber-3 Pattern (ZW3), and Southern Annular Mode (SAM) were correlated with the mean wave parameters using the Pearson’s correlation coefficient and the wavelet spectral analysis. Moreover, mean annual and inter-annual variabilities and trends in Hₛ were computed for the 44-year period. In general, larger annual and inter-annual variabilities were found along the coastline, in regions dominated by local winds. An increasing trend in Hₛ was found around the country, with values varying between 1 and 6 cm/decade at the shoreline. The largest trends in Hₛ were detected to the south of 48°S, suggesting a relationship with the trend toward a positive SAM. The wave parameters showed a strong connection with seasonal to decadal variabilities in the SAM throughout the period analysed. In addition, larger waves were observed during extreme ENSO and IOD events at inter-annual timescales, while they were more evident at intra-seasonal and seasonal timescales in the correlations with the ZW3. Negative phases of the ZW3 and ENSO and positive phases of the IOD, PDO, and SAM were associated with larger waves around most parts of New Zealand.
A detailed climatology of extreme wave events for New Zealand waters was also established, and estimates of Hₛ for up to 100-year return periods were calculated. Although comparisons to buoy data at three locations around New Zealand showed negative biases in the hindcast data, the latter still provided a suitable basis for trend, spatial distribution, and frequency analyses. Results indicate some similarities to patterns previously shown in the mean wave climate, with the largest waves found in southern New Zealand, and the smallest ones observed in areas sheltered from southwesterly swells. The number of extreme events varied substantially throughout the year for the period 1958–2001, while their intensity was more consistent. Extreme events occurred more/less frequently in winter/summer months. The greatest mean annual variability of extreme Hₛ was found on the north coasts of both the North and South Islands, where more locally-generated storms drive the extremes. The inter-annual variability was largest along the north coast of the country and on the east coast of the South Island, suggesting relationships with La Niña-like effects and the SAM, respectively. Furthermore, the known trend for a more positive SAM may explain the increasing number of extreme events on the south and east coasts observed in trend analysis.
Clusters of storm waves contribute disproportionately to coastal erosion hazards because the coastline has insufficient time to recover between events. The change in occurrence of clustered storms and its association with atmospheric oscillation modes were also investigated in New Zealand waters. In order to do so, long-term averages of cluster parameters (number of storms within the cluster, potential for coastal erosion, and cluster duration) were firstly assessed. Then, the relationships between clustering and the ENSO, IOD, ZW3, PDO, and SAM were explored through correlation analysis over several timescales. Clusters were more frequently observed to the northeast of New Zealand and on the central eastern coast of the South Island. The most vulnerable regions to cluster-induced coastal erosion were southern New Zealand and the northwestern coast, which resulted from steady southwesterly swells, although clusters with the longest duration occurred on the east coast of the South Island. Trends suggest that clusters have incorporated more storms, have become more hazardous, and have increased in duration, particularly along the South Island coastline. Although these trends may be sensitive to the reanalysed wind fields used to force the wave hindcast, they reflect trends in the ENSO, PDO, and SAM. Stronger southwesterly winds during El Niño (negative ENSO) and El Niño-like conditions (positive IOD/PDO) generated more clustered storms mainly on the southwestern coast of New Zealand, whereas increases in clustering were observed on the north coast during La Niña and La Niña-like conditions (stronger northeasterly winds). Higher occurrence of clustering was also evident on the west coast during the strong atmospheric zonal flow associated with negative ZW3. Lastly, strengthened westerlies related to positive SAM led to increased clustering primarily to the south of New Zealand.
The last aspect of the wave climate around New Zealand explored in this thesis was the modulation of Hₛ variability by wind anomalies associated with the co-occurrence of the Madden-Julian Oscillation (MJO) and ENSO. For this purpose, Hₛ and wind anomalies composites were created using 23 years (1979–2002) of the wave hindcast data and ERA-40 winds. Composites were calculated for November–March periods, when simultaneous ENSO-MJO phase pairs are potentially most active. Results showed striking features: El Niño-related wave conditions (which consist of increased Hₛ along the west and south coasts of New Zealand) are reinforced during MJO phase 8, whereas the wave conditions associated with La Niña (which consist of larger Hₛ along the north coast) are enhanced during MJO phase 6; Similar wave anomalies are generated during opposing ENSO phases (La Niña and El Niño) when these are combined with MJO phases 3 and 5, respectively; The majority of statistically significant Hₛ anomalies disappear from the study area
during El Niño-MJO phase 6 and La Niña-MJO phase 4, showing the neutralising nature of some phase combinations; Lastly, negative Hₛ anomalies are experienced during El Niño-MJO phase 4, in contrast to the positive anomalies expected during El Niño events. These results clearly show the importance of remote forcing to wave anomalies in the New Zealand region and highlight the need to assess atmospheric and oceanic conditions considering multiple climate oscillations.
This thesis has shown that the wave climate around New Zealand is affected by a range of atmospheric conditions, which have significantly different impacts along the coastline. All these conditions should be taken into account in order to mitigate future hazards. Therefore, the results presented here may assist coastal communities and stakeholders as well as offshore activities around the country in better prepare for potential impacts. Additionally, these results contribute to enhancing the research community knowledge of wave climatology in an area with recognised

year: 2018

title: The Research Data Landscape in Aotearoa New Zealand: A report undertaken in partnership with the Aotearoa New Zealand Committee on Data in Research (CoDiR)

authors: Sterling, Rogena

abstract: Data and datasets are often described as a core strategic asset for Aotearoa New Zealand (Aotearoa) and indispensable for the government’s ambition of being a small nation with an advanced, adaptive, and inclusive economy. In the context of our research, science and innovation (RSI) ecosystem, the value of data cannot be overstated. 
The purpose of this report is to provide a review (the Review) of Aotearoa’s research data landscape. Research data are data that are used as primary sources to support technical or scientific enquiry, research, or artistic activity; as evidence in the research process; and/or are commonly accepted in the
research community as necessary to provide a foundation for, or validate research findings and results. The Review covers four core areas:

• Te Tiriti o Waitangi and data sovereignty
• research data ecosystems
• research data infrastructure
• research data cultures.

Although informed by international data landscape reviews, this Review emphasises the unique considerations and structural features of the Aotearoa data landscape. Based on our analysis and observations from a series of targeted workshops, we provide a set of recommendations on how to strengthen the system and advance shared aspirations for better outcomes. The recommendations are organised under the five headings from Nosek’s Pyramid of Social Change, setting out a phased strategy for culture and behaviour change. In implementing these recommendations, we recognise that the articles of Te Tiriti should be embedded throughout, consistent with sector requirements (MBIE, 2023d).

text: The Research Data Landscape in Aotearoa New Zealand: A report undertaken in partnership with the Aotearoa New Zealand Committee on Data in Research (CoDiR) Data and datasets are often described as a core strategic asset for Aotearoa New Zealand (Aotearoa) and indispensable for the government’s ambition of being a small nation with an advanced, adaptive, and inclusive economy. In the context of our research, science and innovation (RSI) ecosystem, the value of data cannot be overstated. 
The purpose of this report is to provide a review (the Review) of Aotearoa’s research data landscape. Research data are data that are used as primary sources to support technical or scientific enquiry, research, or artistic activity; as evidence in the research process; and/or are commonly accepted in the
research community as necessary to provide a foundation for, or validate research findings and results. The Review covers four core areas:

• Te Tiriti o Waitangi and data sovereignty
• research data ecosystems
• research data infrastructure
• research data cultures.

Although informed by international data landscape reviews, this Review emphasises the unique considerations and structural features of the Aotearoa data landscape. Based on our analysis and observations from a series of targeted workshops, we provide a set of recommendations on how to strengthen the system and advance shared aspirations for better outcomes. The recommendations are organised under the five headings from Nosek’s Pyramid of Social Change, setting out a phased strategy for culture and behaviour change. In implementing these recommendations, we recognise that the articles of Te Tiriti should be embedded throughout, consistent with sector requirements (MBIE, 2023d).

year: 2023