The last decade has featured a seemingly anomalous number of extreme weather events,
including heat waves and cold snaps. These extreme events can be expensive and dangerous, yet
their behavior does not necessarily follow that of the mean, and they are often ill-predicted.
Changes in extremes are closely connected to changes in temperature distributions, and
I am exploring the spatial variability in the changes of different percentiles in temperature
using ground-based station data. It appears that many of these changes can be explained by the underlying
non-normality in surface temperature distributions. Finally, I am interested in
the potential for long-lead predictability of specific types of extreme events based on
climatic precursors, with a current focus on heat waves in the Eastern US.
Collaborators: Andy Rhines, Martin Tingley, Peter Huybers
Huybers, P, KA McKinnon, A Rhines, and MP Tingley, 2014. U.S. daily temperatures: the meaning of extremes in the context of non-normality. Journal of Climate 27 (19), 7368-7384.
The seasonal cycle in surface temperature may be one of the most familiar aspects of climate,
with effects ranging from animal migration to crop planting to tourism. The annual range of
temperature in many locations is larger than the difference between glacial and interglacial
times, and certainly larger than the present-day anthropogenic warming signal.
I am interested in understanding the processes most important for
determining the climatological seasonal cycle, and their implications for future
changes in seasonality.
Collaborators: Zan Stine, Peter Huybers
McKinnon, KA, AR Stine, and P Huybers, 2013. The spatial structure of the annual cycle in surface temperature: amplitude, phase and Lagrangian history. Journal of Climate 26 (20), 7852-7862.
Climate change is often discussed with reference to changes in the global mean temperature,
but the spatially-resolved pattern of warming has more relevance to the on-the-ground
experience of climate change. Understanding the observed pattern of warming has implications
for the balance of radiative forcing, feedbacks, transport, and heat storage in causing
temperature change. The presently-observed spatial pattern of temperature change is similar
to the amplitude of the seasonal cycle, suggesting that similar physical processes are
at play on both timescales.
Collaborators: Peter Huybers
McKinnon, KA and P Huybers, 2014. On using the seasonal cycle to interpret extratropical temperature changes since 1950. Geophysical Research Letters 41 (13), 4676-4684.
Mountain glaciers respond to changes in temperature and precipitation, and can provide
a record of past climate changes in their moraines. Interpreting these moraine records,
however, requires a careful consideration of the local environment of the glacier, including
the coupling between the ice and solid earth via erosion, transport, and deposition of
sediment, which I have previously explored in the context of the Last Glacial Maximum
Pukaki Glacier in New Zealand. Mountain glaciers are also responding to modern-day
climate change, making monitoring increasingly important. During (Northern Hemisphere)
spring 2014, I participated in a
small field campaign using drones to take thousands of
aerial photos of Tasman Glacier, which can be stitched together to make a three-dimensional
digital elevation model of the glacier for use in velocity modeling and long-term volume
monitoring. More blog entries about the campaign are here.
Collaborators: Brian Anderson, Andrew Mackintosh, Tom McKinnon, David Barrell
McKinnon, KA, AN Mackintosh, BM Anderson, and DJA Barrell, 2012. The influence of sub-glacial bed evolution on ice extent: a model-based evaluation of the Last Glacial Maximum Pukaki glacier, New Zealand. Quaternary Science Reviews 57, 46-57.
The climate system is extremely complex, with important processes spanning a wide range of spatial and temporal scales. Creating models that are also complex, however, leads to challenges in interpreting both the actual climate system and the climate models. Model evaluation through established frameworks in information theory and machine learning can assist in choosing models that maximize information content and avoid overly high variance.