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Ruth Lorenz, Andrew J. Pitman, Annette L. Hirsch, and Jhan Srbinovsky

Abstract

Land–atmosphere coupling can strongly affect climate and climate extremes. Estimates of land–atmosphere coupling vary considerably between climate models, between different measures used to define coupling, and between the present and the future. The Australian Community Climate and Earth-System Simulator, version 1.3b (ACCESS1.3b), is used to derive and examine previously used measures of coupling strength. These include the GLACE-1 coupling measure derived on seasonal time scales; a similar measure defined using multiyear simulations; and four other measures of different complexity and data requirements, including measures that can be derived from standard model runs and observations. The ACCESS1.3b land–atmosphere coupling strength is comparable to other climate models. The coupling strength in the Southern Hemisphere summer is larger compared to the Northern Hemisphere summer and is dominated by a strong signal in the tropics and subtropics. The land–atmosphere coupling measures agree on the location of very strong land–atmosphere coupling but show differences in the spatial extent of these regions. However, the investigated measures show disagreement in weaker coupled regions, and some regions are only identified by a single measure as strongly coupled. In future projections the soil moisture trend is crucial in generating regions of strong land–atmosphere coupling, and the results suggest an expansion of coupling “hot spots.” It is concluded that great care needs to be taken in using different measures of coupling strength and shown that several measures that can be easily derived lead to inconsistent conclusions with more computationally expensive measures designed to measure coupling strength.

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Alberto Troccoli, Karl Muller, Peter Coppin, Robert Davy, Chris Russell, and Annette L. Hirsch

Abstract

Accurate estimates of long-term linear trends of wind speed provide a useful indicator for circulation changes in the atmosphere and are invaluable for the planning and financing of sectors such as wind energy. Here a large number of wind observations over Australia and reanalysis products are analyzed to compute such trends. After a thorough quality control of the observations, it is found that the wind speed trends for 1975–2006 and 1989–2006 over Australia are sensitive to the height of the station: they are largely negative for the 2-m data but are predominantly positive for the 10-m data. The mean relative trend at 2 m is −0.10 ± 0.03% yr−1 (−0.36 ± 0.04% yr−1) for the 1975–2006 (1989–2006) period, whereas at 10 m it is 0.90 ± 0.03% yr−1 (0.69 ± 0.04% yr−1) for the 1975–2006 (1989–2006) period. Also, at 10 m light winds tend to increase more rapidly than the mean winds, whereas strong winds increase less rapidly than the mean winds; at 2 m the trends in both light and strong winds vary in line with the mean winds. It was found that a qualitative link could be established between the observed features in the linear trends and some atmospheric circulation indicators (mean sea level pressure, wind speed at 850 hPa, and geopotential at 850 hPa), particularly for the 10-m observations. Further, the magnitude of the trend is also sensitive to the period selected, being closer to zero when a very long period, 1948–2006, is considered. As a consequence, changes in the atmospheric circulation on climatic time scales appear unlikely.

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Annette L. Hirsch, Jatin Kala, Andy J. Pitman, Claire Carouge, Jason P. Evans, Vanessa Haverd, and David Mocko

Abstract

The authors use a sophisticated coupled land–atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%–20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.

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