I'm a postdoctoral scientist in the Climate Physics group at ETH Zurich working in the frame of the Horizon 2020 project EUCP. My research focuses on the investigation of uncertainties in European climate model projections. I also have some expertise in the analysis of atmospheric blocking and I'm passionate about science to public communication and open science.
Brunner, L., N. Schaller, J. Anstey, J. Sillmann,
A. K. Steiner (2018): Dependence of present and future European
temperature extremes on the location of atmospheric
blocking. Geophys. Res. Lett. 45,
Unterberger, C., L. Brunner, S. Nabernegg, K. Steininger, A. K. Steiner, E.
Stabentheiner, S. Monschein, and H. Truhetz (2018): Spring frost risk
for regional apple production under a warmer
climate. PLoS ONE 13, DOI: 10.1371/journal.pone.0200201
Brunner, L. and A. K. Steiner (2017): A global perspective on
atmospheric blocking using GPS radio occultation – one decade of
observations. Atmos. Meas. Tech.,
Brunner, L., A. K. Steiner, B. Scherllin-Pirscher, and M. W. Jury
(2016): Exploring atmospheric blocking with GPS radio occultation
observations, Atmos. Chem. Phys. 16.7, 4593-4604,
Brunner L. (2018): A new perspective on atmopsheric blocking
from observations – detection, analysis, and impacts (PhD
thesis). Wegener Center Verlag Graz, Scientific Report
Mohankumar, S. E. P., K. Mintz-Woo, M. Damert, L. Brunner, and
J. Eise (2018): Blogging Climate Change: A Case Study, In:
Addressing the Challenges in Communication Climate Change Across
Various Audiences DOI: 10.1007/978-3-319-98294-6
Brunner L. (2014): Stratospheric ozone and temperature
evolution over the past decades (Master's thesis). Wegener
Center Verlag Graz, Scientific Report No. 59-2014,
I work in the frame of a EU Horizon 2020 project called European Climate Prediction (EUCP) system. The project aims at providing reliable information about future European climate, which can be used by decition-makers.
While the warming of the climate system through the emission of man-made greenhouse gases is clear from observations and models, predictions of the future climate face a range of uncertainties. In the following I'm going to introduce some of the most important sources of uncertainty and how my research can help to reduce them.
Natural variability stems from the fact that the Earths
climate is an extremely complex and chaotic system. The
animation below shows global mean temperature for the years 1900 to
2005 from one selected climate model. Ignoring a possible trend for
now, the (seemingly random) fluctuations are refered to as natural (or unforced, internal)
variability and constitute one important source of uncertainty. In
fact, natural variability cannot really be reduced since it is part
of the climate system (however, one could potentially select temporal
and spatial scales with lower natural variability – more about
Deser at al. 2012a (paywall), Deser at al. 2012b, Martel et al. 2018
Scenario uncertainty araises because climate scientist cant'
make predictions about decitions taken by politics and society. Will
we continue to emit greenhouse gases as we did in the past? Or will
we manage to reach the goals set by the Paris
Agreement, hence limiting global warming to well below
2°C? In order to be able to make statements about the future
climate we use projections based on different paths our society
(read: our emissions) might take. The animation below shows the
possible deveopment of global mean temperature between 2006 and 2100
based on three such assumptions.
IPCC 2007, Moss et al. 2010 (paywall), IPCC 2010, IPCC 2017
Model uncertainty accounts for the fact that climate models
can never completely reproduce reality (I've already mentioned that
the climate system is extremely complex). Models therefore use
approximations to represent certain processes. Different models use
different approximations and therefore yield slightly different
results. The animation below consequtively adds more models for each
of the three scenarios, resulting in an representaion of uncertainty
due to imperfect modeling.
Smith et al. 2002 (paywall), Knutti 2008, Rowlands et al. 2012 (paywall),
Wrap-up part I: I indroduced the three main sources of uncertainty we face when predicting Earths future climate. The animation below puts it all together and shows the relative contributions from each of the three sources to the total uncertainty. As can be seen, the contributions are not constant over time. For the near future internal variability still plays an important role. Model uncertainty is most important on an intermediate time scale, while predictions for the end of the century are dominated by the scenario uncertainty.
Based on data from the ETH CMIP-5 archive. Figure 1 shows historical annual mean, global mean temperature anomalies from the CNRM-CM5 model. Figure 2 additionally shows the RCPs 2.6, 4.5, and 8.5. Figure 3 shows up to 28 models. Figure 4 shows the CNRM-CM5 model as an representation for internal variability, the standard deviation from the multi-model mean as a representation for model uncertainty, and the three RCPs as a representation for scenario uncertainty. The small figure inside figure 4 shows the relative contributions from the three sources of uncertainty based on a method proposed by Hawkins and Sutton 2009
Area size has a large impact on uncertainty, as mentioned at the beginning. However, the area which is investigated can often not be freely chosen. The smallest area which can be studied is limited by the model resolution (about 1° latitude and longitude in the latest global climate models – corresponding to about 110 km x 80 km at mid-latitudes; see here for a calculator) and the largest area is the global average. The actual area is often predetermined by the question(s) which we try to answer. The figure below allows to change the area to see the effect on temperature. The area size increases from one grid cell (shown is the grid cell closest to Zurich, Switzerland), over a country level average, to an average over all of Europe. Increasing the area reduces the (internal) variability but at the same time regional information is lost due to the averaging.
Time resolution also has a major influence on the variability. Particularly on time scales below one year, the annual cycle (if not removed) becomes the domination source of variability as can be seen in the figure below. Equivalent to the area size the time resolution is often predetermined by the problem. [Use the 'Zoom' function to see the details on sub-annual time scales].
Warp-up part II: By selecting 'proper' spatial and temporal scales, the uncertainty one has to deal with can be significantly reduced. However, the resolutions are often restricted by the question (the development of seasonal temperature in Switzerland, for example, can probably not be addressed using global-mean, annual-mean data). But often there is some space for adjustment and optimising the resolution of the data can help in getting a clearer signal.
Based on data from the CNRM-CM5 model (as in part I). Figure 1 shows winter (December, January, February – DJF) temperatures averaged over different areas. The area average is implemented as Gaussian-weighted mean with varying sigma values centred at the grid cell closest to Zurich. Figure 2 shows global, daily temperatures for different running mean windows: 1 day (day), 7 days (week), 31 days (month), 93 days (season), 181 days (6 months), 270 days (9 months), and 365 days (year).