Logo image
Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere–land surface model
Journal article   Open access   Peer reviewed

Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere–land surface model

R. Lorenz, A.J. Pitman, M.G. Donat, A.L. Hirsch, J. Kala, E.A. Kowalczyk, R.M. Law and J. Srbinovsky
Geoscientific Model Development, Vol.7(2), pp.545-567
2014
pdf
climate_extreme_indices.pdfDownloadView
Published (Version of Record)CC BY V4.0 Open Access
url
Free to Read *No subscription requiredView

Abstract

Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and amplifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#6 Clean Water and Sanitation
#13 Climate Action
#14 Life Below Water

Source: InCites

Metrics

133 File views/ downloads
58 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
Citation topics
8 Earth Sciences
8.19 Oceanography, Meteorology & Atmospheric Sciences
8.19.7 Hydroclimatic Modeling
Web Of Science research areas
Geosciences, Multidisciplinary
ESI research areas
Geosciences
Logo image