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  • Author or Editor: B. L. Li x
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A. M. Makarieva
,
V. G. Gorshkov
,
D. Sheil
,
A. D. Nobre
,
P. Bunyard
, and
B.-L. Li

Abstract

The influence of forest loss on rainfall remains poorly understood. Addressing this challenge, Spracklen et al. recently presented a pantropical study of rainfall and land cover that showed that satellite-derived rainfall measures were positively correlated with the degree to which model-derived air trajectories had been exposed to forest cover. This result confirms the influence of vegetation on regional rainfall patterns suggested in previous studies. However, the conclusion of Spracklen et al.—that differences in rainfall reflect air moisture content resulting from evapotranspiration while the circulation pattern remains unchanged—appears undermined by methodological inconsistencies. Here methodological problems are identified with the underlying analyses and the quantitative estimates for rainfall change predicted if forest cover is lost in the Amazon. Alternative explanations are presented that include the distinct role of forest evapotranspiration in creating low-pressure systems that draw moisture from the oceans to the continental hinterland. A wholly new analysis of meteorological data from three regions in Brazil, including the central Amazon forest, reveals a tendency for rainy days during the wet season with column water vapor (CWV) exceeding 50 mm to have higher pressure than rainless days, while at lower CWV, rainy days tend to have lower pressure than rainless days. The coupling between atmospheric moisture content and circulation dynamics underlines that the danger posed by forest loss is greater than suggested by consideration of moisture recycling alone.

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R. D. Koster
,
S. P. P. Mahanama
,
T. J. Yamada
,
Gianpaolo Balsamo
,
A. A. Berg
,
M. Boisserie
,
P. A. Dirmeyer
,
F. J. Doblas-Reyes
,
G. Drewitt
,
C. T. Gordon
,
Z. Guo
,
J.-H. Jeong
,
W.-S. Lee
,
Z. Li
,
L. Luo
,
S. Malyshev
,
W. J. Merryfield
,
S. I. Seneviratne
,
T. Stanelle
,
B. J. J. M. van den Hurk
,
F. Vitart
, and
E. F. Wood

Abstract

The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.

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