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Hydroclimatic projections for the Murray‐Darling Basin based on an ensemble derived from Intergovernmental Panel on Climate Change AR4 climate models
Author(s) -
Sun Fubao,
Roderick Michael L.,
Lim Wee Ho,
Farquhar Graham D.
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009829
Subject(s) - quantile , climate change , climatology , environmental science , precipitation , ensemble average , climate model , range (aeronautics) , baseline (sea) , series (stratigraphy) , structural basin , downscaling , representative concentration pathways , statistics , mathematics , meteorology , econometrics , geography , geology , paleontology , oceanography , materials science , composite material
We assess hydroclimatic projections for the Murray‐Darling Basin (MDB) using an ensemble of 39 Intergovernmental Panel on Climate Change AR4 climate model runs based on the A1B emissions scenario. The raw model output for precipitation, P , was adjusted using a quantile‐based bias correction approach. We found that the projected change, Δ P , between two 30 year periods (2070–2099 less 1970–1999) was little affected by bias correction. The range for Δ P among models was large (∼±150 mm yr −1 ) with all–model run and all‐model ensemble averages (4.9 and −8.1 mm yr −1 ) near zero, against a background climatological P of ∼500 mm yr −1 . We found that the time series of actually observed annual P over the MDB was indistinguishable from that generated by a purely random process. Importantly, nearly all the model runs showed similar behavior. We used these facts to develop a new approach to understanding variability in projections of Δ P . By plotting Δ P versus the variance of the time series, we could easily identify model runs with projections for Δ P that were beyond the bounds expected from purely random variations. For the MDB, we anticipate that a purely random process could lead to differences of ±57 mm yr −1 (95% confidence) between successive 30 year periods. This is equivalent to ±11% of the climatological P and translates into variations in runoff of around ±29%. This sets a baseline for gauging modeled and/or observed changes.