CAIT Country greenhouse gas emissions, 1990–2013

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2511
9
Added
13 Oct 2017

This dataset was first added to MfE Data Service on 13 Oct 2017.

Emissions data are from the Climate Analysis Indicators Tool (CAIT) version 2.0 produced by the World Resources Institute (WRI). WRI compiles country-level emissions data from governmental sources, complemented by non-governmental sources “based on criteria such as completeness and relative accuracy and country datasets are produced by applying a consistent methodology.” For detailed information see World Resources Institute (2015).
Data compiled are obtained from government reporting and complemented by a variety of non-governmental data sources.
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 89443
Data type Table
Row count 90240
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Growing degree days trend assessment, by site, 1972/3–2015/6

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Creative Commons Attribution 4.0 International

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2931
18
Added
18 Oct 2017

This dataset was first added to MfE Data Service on 18 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 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 89481
Data type Table
Row count 30
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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2611
13
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

Melanoma registration rates, 1948–2015

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Creative Commons Attribution 4.0 International

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2623
10
Added
14 Oct 2017

This dataset was first added to MfE Data Service on 14 Oct 2017.

New Zealand and Australia have the world’s highest rates of melanoma, the most serious type of skin cancer. Melanoma is mainly caused by exposure to ultraviolet (UV) light, usually from the sun. New Zealand has naturally high UV levels, especially during summer.
The risk of developing melanoma is affected by factors such as skin colour and type, family history, and the amount of sun exposure. Melanoma can affect people at any age, but the chance of developing a melanoma increases with age. We report on age-standardised rates of melanoma to account for the increasing proportion of older people in our population.
Our data on melanoma registrations come from the New Zealand Cancer Registry and the Ministry of Health's Mortality Collection. The passing of the Cancer Registry Act 1993 and Cancer Registry Regulations 1994 led to significant improvements in data quality and coverage (Ministry of Health, 2013). A sharp increase in registrations after 1993 is likely to have been related to these legislative and regulatory changes; for this reason we have only analysed data from 1996.
2014–15 data are provisional and subject to change.
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 89458
Data type Table
Row count 204
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Trends in ozone concentrations, 1978–2017

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Creative Commons Attribution 4.0 International

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2780
5
Added
14 Oct 2017

This dataset was first added to MfE Data Service on 14 Oct 2017.

NIWA supplied ozone data in two forms, with different starting dates:
- measurements made using a Dobson spectrophotometer (number 72), from 1987
- data assimilated from satellite measurements recalibrated against the global Dobson network, from 1978.
NIWA takes measurements using the Dobson spectrophotometer 72 under clear-sky, direct sunlight conditions at Lauder in Otago. There are gaps in the time series due to days with cloud, rain, or too much wind. However, over the whole period, each individual calendar day of the year was measured. This allows us to calculate statistics based on the day of the year.
The trend 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 89465
Data type Table
Row count 3
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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2821
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

Global and New Zealand temperature anomalies, 1909–2016

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2396
9
Added
14 Oct 2017

This dataset was first added to MfE Data Service on 14 Oct 2017.

This dataset compares temperatures anomalies from NIWA's 'seven-station' temperature series with three global temperature series.
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 89452
Data type Table
Row count 855
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Greenhouse gas concentrations at Baring Head, 1972–2016

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Creative Commons Attribution 4.0 International

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2680
11
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.
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 89412
Data type Table
Row count 1103
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Forest carbon stocks trends, 1990–2015

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2599
9
Added
12 Oct 2017

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

New Zealand’s indigenous and exotic forests absorb carbon dioxide (CO2) from the atmosphere through photosynthesis and store the carbon as biomass and in the soil. On average, more than twice as much carbon per hectare is stored in New Zealand’s mature indigenous forests than in exotic forests planted for wood production. Regenerating indigenous forests are also an important store of carbon, adding carbon every year as they grow. Total carbon stored in exotic forests will fluctuate over decades as the forests grow from seedlings to mature trees, are harvested, and replanted. Because CO2 is the major driver of climate change, forests provide important mitigation services and help New Zealand meet its climate change commitments.
The trend 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 89410
Data type Table
Row count 2
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Southern Annular Mode monthly values, January 1979–December 2016

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2671
18
Added
12 Oct 2017

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

A consistent band of westerly wind flows across the Southern Hemisphere and circles the South Pole. The Southern Annular Mode (SAM) describes how this band moves, either north towards the equator (negative phase) or south towards Antarctica (positive phase). A negative phase typically causes increased westerlies, unsettled weather, and storms in New Zealand. A phase can last several weeks, but changes can be rapid and unpredictable.
The SAM is one of three climate oscillations that affect our weather. The resulting changes in air pressure, sea temperature, and wind direction can last for weeks to decades, depending on the oscillation.
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 89384
Data type Table
Row count 456
Services Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed
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