Research aims

Learn more about the research aims of the Whakahura project.

Find out about the five main aims of research in the Whakahura project:

Extreme event attribution

When extreme weather events occur, often the question arises whether the event is due to climate change. Extreme event attribution tries to answer this question.

Extreme event attribution is a relatively new field in climate science. It sits at the intersection of statistics, atmospheric physics, and geography. It aims to describe how much the probability of an event, like a tropical storm, depends on climate change. IPCC assessments use attribution science to look at the effect of global and regional temperature increases.

Whakahura is unique because we are bringing together the attribution of extreme events with economics. Our aim is to figure out how much of an extreme event’s costs are because of the human fingerprint on the climate.

Determining probabilities

To determine whether the probability of extremes has increased we start by comparing different types of long time series data (for example, observations and climate models).

The second step involves determining the probability of the extreme event in the recent climate in the “normal” period and its probability in the past when the concentration of greenhouse gases (GHGs) was much lower. We use n model-based approaches to simulate and compare weather and climate phenomena with and without human-caused changes in GHGs.

To determine the probabilities, we use observational data, fit a a generalised extreme value distribution (or other statistical distribution), and determine confidence intervals. This allows us to work out whether there are significant changes or trends in the occurrence of these extreme events.

Even if climate change is detected, for causal attribution we need a link with a factor that is clearly influenced by increasing GHGs. The most obvious climate variable is temperature. Using a statistical approach, we factor in global warming by allowing the fit to the distribution to be a function of the global mean surface temperature (GMST). If there is a clear relation between the climate index under study and the GMST (and thus with the increase of GHGs) the probability of the climate index under study will have changed over time.

Extreme event emergence

Recent work shows much of the planet will experience higher frequency and higher impact weather events in coming decades. Climate change emergence is the study of how anthropogenic (human-induced) climate change signals rise above natural variability (noise). The pattern arising from signal to noise ratios is called the emergence pattern. In the Whakahura Programme, we are looking at extreme weather events in particular.

The emergence pattern is closely related to many acute climate stresses, via extreme weather, heatwaves, crop-growing conditions, and so on. We propose to deepen understanding of the consequences of emergent signals, both for climate variables that characterise extreme weather and for natural systems such as forest ecology and river systems. Finally, we propose to better quantify the economic consequences of the emerging human fingerprints on human systems.

Statistical tools

We will use the standard method of Hawkins and Sutton (2012), which describes local climate as a linear function of a covariate signal (usually a smoothed global average surface temperature) and local variability. We will apply this method to a variety of local climate measures over Aotearoa New Zealand, such as local average temperatures, average total precipitation, extreme temperatures, and extreme precipitation. We will also apply this method to regional climate measures related to the occurrence of extreme weather over Aotearoa, such as the rate of La Niña events in the tropical Pacific.

We will vary the linear function approach to examine the various contributions to extreme weather emergence signals. In particular, we will test the usefulness of multiple alternative covariates, such as ones based on the response to increasing greenhouse gas concentrations and to varying stratospheric ozone concentrations. We will also identify which aspects of the local noise are manifestations of variations in larger-scale modes of climate variability, such as the El Niño/Southern Oscillation, and how those modes compare to the processes responsible for generating local extreme weather, through methods such as correlation analysis.

Vision Mātauranga

We are looking at understanding extreme weather damage from a te ao Māori perspective. We do this through:

  1. Collecting stories and images from local iwi and hapū, and planning an empirical element creating an index to begin to quantify this. There is also a data science portion compiling kōrero, images, and videos about the impact of disasters on whanau, hapū, and iwi.
  2. Working with iwi and hapū, drawing on relationships with te taiao (the environment), to assess from a kaupapa Māori perspective the damage caused by extreme weather events on assets (mahinga kai, ngahere, awa, and whenua).
  3. Identifying attributes and measures to define damage from extreme weather with iwi and hapū researchers:
    • from He Oranga ma ngā Uri Tuku Iho Trust (Ngāti Porou) to look at forestry and the impacts of non-native tree planting in Tologa Bay, and
    • from Ngāi Tuahuriri rūnanga (Ngāi Tahu) to look at what happens between the banks of the Waimakariri, including mahinga kai and other elements of infrastructure particular to te ao Māori that may be affected by flooding

Policy implications

Work in this area will provide a greater understanding of damage through a hauora/oranga lens, so that policy can be developed that considers the interests of iwi and hapū. Tapping into local knowledge and mātauranga will help inform responses to disaster management. It will also help to empower iwi and hapū decision makers in resource management to actively rediscover, share, and implement kaupapa Māori-based solutions for disaster policy and planning.

Economic impact

Disaster database

The first step in this research stream is to create a disaster loss database. This addresses a longstanding gap in New Zealand and brings us into line with other countries. The database sets the evidential foundation for the cost, damages, and vulnerability aspects of the programme.

Māori perspectives on damage will be integrated by collecting Māori stories. This means events which have been felt deeply, and caused harm as defined by Māori people, are identified and included rather than only those which satisfy traditional climate and economic definitions of “extreme” (e.g. precipitation amount or financial asset value).

The integration of physical understanding with economic costs and cultural perspectives on damage will create a disaster loss and damage database that will be an invaluable asset for all the New Zealand science and policy community.

Projected losses

The second step in this research stream will build on the new disaster loss database. By assessing attributable risk for past events, we will create a unique scientific asset that will allow us to estimate both the total and climate change component costs of these events.

Combining this new database with our new knowledge on the emergence of extremes, we will learn how and when the risks of extremes, droughts, and other events will change. We will also develop a model of disaster damage that will be useful in developing detailed projections of future losses on a sector basis. Through our hydrology and ecology case studies we will look at the end-to-end ability of our new information to drive decision making around climate extremes.

Extreme event diagnosis

As greenhouse gases continue to rise in the atmosphere, changes in atmospheric dynamics, thermodynamics, and ocean heat content are likely to increase the frequency and severity of extreme weather events (EWEs).

The objective of this research stream is to improve the diagnosis and simulation of past EWEs in Aotearoa. It draws on meteorology, atmospheric physics, and mātauranga Māori.

There are many challenges exploring past and expected future changes in EWEs and their downstream systemic impacts. EWEs are, by their nature, rare and we have only one instance of Earth’s climate history from which to diagnose their changes. As such, a variety of climate model simulations of both the past and future evolution of Earth’s climate will be conducted and analysed in support of this research.

Our process

We will first add historical EWEs to an existing database of events and then label these events for their meteorological characteristics. Exogenous variables describing risk will be appended to these database entries to create extended labels that can be used as predictors of the financial costs associated with an EWE.

To improve our ability to simulate EWEs in numerical weather prediction (NWP) models and in regional climate models (RCMs), we will identify the atmospheric and oceanic phenomena and processes (both dynamical and thermodynamic) that lead to EWEs. Then, focussing on those processes and phenomena, we will evaluate our ability to simulate them in models.

This research will also allow us to improve our ability to adapt to the extremes of climate that we will face in the future. Flooding, drought, and fire are all hazards for which forecasting can improve our ability to respond and reduce our economic cost.