**Matz Haugen, Stanford University**

## What to Expect

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatio-temporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling.

The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows testing the efficacy of the method with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

## Speaker Bio

Matz Haugen was born and raised in Oslo, Norway, did his undergraduate degree in Physics from McGill University, in Montreal, Canada and finished his PhD in Statistics and Earth Sciences at Stanford University working under Bala Rajaratnam and Paul Switzer. His thesis work involved signal extraction from noisy time series data with application to the environmental sciences. He also collaborated with Noah Diffenbaugh on the topic of extreme weather attribution. Matz also holds a Master’s degree in Physics from Cambridge University.

After finishing his PhD, Matz went to the University of Chicago as a postdoctoral scholar to work under Michael Stein on furthering our understanding of how the climate will change for the next 100 years by using ensemble climate model output and statistical analysis with particular emphasis given to characterizing changes in the extremes of temperature distributions in North America.

For the last two years, Matz has been working in the industry working on understanding economic activity at retail stores through satellite imagery for a company called Orbital Insight building data transformation pipelines serving data on a daily cadence.