Trends in greenhouse gas concentrations at Baring Head, 1972–2016

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Creative Commons Attribution 4.0 International

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3200
4
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

We report on GHG concentrations in ‘clean air’ measured at Baring Head, near Wellington. These measurements give us a good idea of global concentrations and help us infer long-term impacts on ocean acidity, temperature, sea level and glaciers.
Trends were assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level.
More information on this dataset and how it relates to our Environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89413
Data type Table
Row count 3
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days trend assessment, for New Zealand, the North Island, and the South Island, 1972/3–2015/6

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Creative Commons Attribution 4.0 International

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3108
11
Added
17 Oct 2017

This dataset was first added to MfE Data Service on 17 Oct 2017.

Growing degree days (GDD) measures the amount of warmth available for plant and insect growth and can be used to predict when flowers will bloom and crops and insects will mature. GDD counts the total number of degrees Celsius each day is above a threshold temperature. In this report we used 10 degrees Celsius. Increased GDD means that plants and insects reach maturity faster, provided that other conditions necessary for growth are favourable, such as sufficient moisture and nutrients. As a measure of temperature, GDD experiences short-term changes in response to climate variations, such as El Niño, and in the longer-term is affected by our warming climate.
Growing degree days (GDD) counts the number of days that are warmer than a threshold temperature (Tbase) in a year. GDD is calculated by subtracting the Tbase from the average daily temperature (maximum plus minimum temperature divided by two). If the average daily temperature is less than Tbase the GDD for that day is assigned a value of zero.
This dataset gives the trend in GDD over growing seasons (July 1 – June 30 of the following year) for New Zealand and the North and South Islands.
Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89476
Data type Table
Row count 3
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days monthly data by site, 1972–2016

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Creative Commons Attribution 4.0 International

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3975
32
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

Growing degree days (GDD) measures the amount of warmth available for plant and insect growth and can be used to predict when flowers will bloom and crops and insects will mature. GDD counts the total number of degrees Celsius each day is above a threshold temperature. In this report we used 10 degrees Celsius. Increased GDD means that plants and insects reach maturity faster, provided that other conditions necessary for growth are favourable, such as sufficient moisture and nutrients. As a measure of temperature, GDD experiences short-term changes in response to climate variations, such as El Niño, and in the longer-term is affected by our warming climate.
This dataset gives the number of GDD per month and calendar year for all 30 sites.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89392
Data type Table
Row count 1290
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days annual growing season averages and totals, 1972/3–2015/6

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Creative Commons Attribution 4.0 International

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You must attribute the creator in your own works.

4522
24
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

Growing degree days (GDD) measures the amount of warmth available for plant and insect growth and can be used to predict when flowers will bloom and crops and insects will mature. GDD counts the total number of degrees Celsius each day is above a threshold temperature. In this report we used 10 degrees Celsius. Increased GDD means that plants and insects reach maturity faster, provided that other conditions necessary for growth are favourable, such as sufficient moisture and nutrients. As a measure of temperature, GDD experiences short-term changes in response to climate variations, such as El Niño, and in the longer-term is affected by our warming climate.
This dataset gives the average number of GDD over growing seasons (July 1 – June 30 of the following year) for New Zealand, the North and South Islands, and for all 30 sites.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89393
Data type Table
Row count 1389
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Frost and warm days, 1972–2016

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Creative Commons Attribution 4.0 International

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3058
22
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

The number of frost and warm days changes from year to year in response to climate variation, such as the warming pattern induced by El Niño. Climate models project we may experience fewer cold and more warm extremes in the future. Changes in the number of frost and warm days can affect agriculture, recreation, and our behaviour, for example, what we do to keep safe on icy roads or whether to use air conditioning to keep cool.
A frost day is when the minimum temperature recorded is below 0 degrees Celsius. It refers to a temperature measured in an instrument screen 1.2 m above the ground rather than a ‘ground frost’. We define a warm day as having a maximum recorded temperature above 25 degrees Celsius. The threshold of 25 degrees Celsius is chosen to represent days where action might be taken to keep cool (eg turn air conditioning on).
This dataset gives the number of frost and warm days per month and calendar year for New Zealand, the North and South Islands, and all 30 sites.
For frost days we have used calendar years. For warm days we have used growing season (July 1 – June 30 of the following year).
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89387
Data type Table
Row count 32667
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Frost and warm days trend assessment, 1972–2016

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Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

3722
14
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

The number of frost and warm days changes from year to year in response to climate variation, such as the warming pattern induced by El Niño. Climate models project we may experience fewer cold and more warm extremes in the future. Changes in the number of frost and warm days can affect agriculture, recreation, and our behaviour, for example, what we do to keep safe on icy roads or whether to use air conditioning to keep cool.
A frost day is when the minimum temperature recorded is below 0 degrees Celsius. It refers to a temperature measured in an instrument screen 1.2m above the ground rather than a ‘ground frost’. We define a warm day as having a maximum recorded temperature above 25 degrees Celsius. The threshold of 25 degrees Celsius is chosen to represent days where action might be taken to keep cool (eg turn air conditioning on).
This dataset gives the trend in frost and warm days for New Zealand, the North and South Islands, and for all 30 sites.
For frost days we have used calendar years. For warm days we have used growing season (July 1 – June 30 of the following year).
Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level.
More information on this dataset and how it relates to our Environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89388
Data type Table
Row count 60
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Campylobacteriosis, cryptosporidiosis, and salmonellosis notifications, 1997–2016

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Creative Commons Attribution 4.0 International

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3038
3
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

Bacteria and parasites are influenced by climate variables, and infection rates may increase in response to climate change and rising temperatures. Campylobacter, Cryptosporidium, and Salmonella are three such organisms that can contaminate our food and water, leading to serious illness. Monitoring the incidence rates of illnesses can help us assess the health risks related to climate change and better prepare for disease outbreaks.
The numbers of notified cases of infection are sourced from EpiSurv, New Zealand’s national notifiable disease surveillance system. Various factors influence disease notification, and therefore the calculation of notifiable disease rates. For example, people are less likely to consult a medical practitioner when an illness is not severe (ESR, 2016a). The number of notified cases vary greatly from year to year due to New Zealand’s small population and low number of cases for some diseases (Environmental Science and Research, 2016). The August 2016 Camplylobacter outbreak in Havelock provides an example of this variation (ESR, 2016b).
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89386
Data type Table
Row count 816
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in number of days with a maximum gust in the 99th percentile, 1972–2016

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Creative Commons Attribution 4.0 International

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You must attribute the creator in your own works.

3889
6
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

Trends in number of days with a maximum gust in the 99th percentile, 1972–2016. The number of days with a maximum gust in the 99th percentile provides information on the frequency of extreme wind events. Percentiles are obtained from all available daily maximum wind gust data. On average, the 99th percentile daily maximum wind gust will be exceeded on approximately 3.6 days per year. Therefore, annual counts higher than this indicate more days than usual with very strong wind gusts recorded; annual counts lower than 3.6 indicate fewer strong wind gust days than usual. By using a percentile threshold we can identify events that are extreme for a particular location. Some places are naturally subject to stronger winds than others, so vegetation can become ‘wind-hardened’ and may have a higher tolerance to high wind gusts (eg a 100 km/hr wind gust may be damaging at one location, but not at another). Using a relative threshold accounts for these differences and better captures extreme wind gust occurrences. The highest maximum gust per year and the average annual highest maximum wind gust both provide information on the magnitude of extreme wind events.
Steady wind can be an important resource, but strong gusts can damage property, topple trees, and disrupt transportation, communications, and electricity. Extreme wind events can occur with frontal weather systems, around strong convective storms such as thunderstorms, and with ex–tropical cyclones. Projections indicate climate change may alter the occurrence of extreme wind events, with the strength of extreme winds expected to increase over the southern half of the North Island and the South Island, especially east of the Southern Alps, and decrease from Northland to Bay of Plenty. Monitoring can help us gauge the potential of, and prepare for, such events.
Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89423
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in maximum highest annual wind gust, 1972–2016

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Creative Commons Attribution 4.0 International

You may use this work for commercial purposes.

You must attribute the creator in your own works.

3711
11
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

Trends in maximum highest annual wind gust, 1972–2016. The number of days with a maximum gust in the 99th percentile provides information on the frequency of extreme wind events. Percentiles are obtained from all available daily maximum wind gust data. On average, the 99th percentile daily maximum wind gust will be exceeded on approximately 3.6 days per year. Therefore, annual counts higher than this indicate more days than usual with very strong wind gusts recorded; annual counts lower than 3.6 indicate fewer strong wind gust days than usual. By using a percentile threshold we can identify events that are extreme for a particular location. Some places are naturally subject to stronger winds than others, so vegetation can become ‘wind-hardened’ and may have a higher tolerance to high wind gusts (eg a 100 km/hr wind gust may be damaging at one location, but not at another). Using a relative threshold accounts for these differences and better captures extreme wind gust occurrences. The highest maximum gust per year and the average annual highest maximum wind gust both provide information on the magnitude of extreme wind events.
Steady wind can be an important resource, but strong gusts can damage property, topple trees, and disrupt transportation, communications, and electricity. Extreme wind events can occur with frontal weather systems, around strong convective storms such as thunderstorms, and with ex–tropical cyclones. Projections indicate climate change may alter the occurrence of extreme wind events, with the strength of extreme winds expected to increase over the southern half of the North Island and the South Island, especially east of the Southern Alps, and decrease from Northland to Bay of Plenty. Monitoring can help us gauge the potential of, and prepare for, such events.
Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89424
Data type Table
Row count 30
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

The annual SOI compared with New Zealand's detrended temperature series, 1908/9–2015/6

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Creative Commons Attribution 4.0 International

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3741
9
Added
12 Oct 2017

This dataset was first added to MfE Data Service on 12 Oct 2017.

The El Niño Southern Oscillation (ENSO) is the movement of warm equatorial water across the Pacific Ocean and the atmospheric response. It occurs every 2–7 years, typically lasting 6–18 months. ENSO has three phases: neutral, El Niño and La Niña. In New Zealand an El Niño phase in summer can bring increased westerly winds, more rain in the west, and drought in the east; in winter it can lead to more cool southerly winds. During a La Niña phase we may experience more north-easterly winds, wetter conditions in the north and east, and higher sea levels.
This dataset relates to annual ENSO and detrended temperature data.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Table ID 89380
Data type Table
Row count 216
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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