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Justin Sheffield, Alan D. Ziegler, Eric F. Wood, and Yangbo Chen

Abstract

A spurious wavelike pattern in the monthly rain day statistics exists within the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis precipitation product. The anomaly, which is an artifact of the parameterization of moisture diffusion, occurs during the winter months in the Northern and Southern Hemisphere high latitudes. The anomaly is corrected by using monthly statistics from three different global precipitation products from 1) the University of Washington (UW), 2) the Global Precipitation Climate Project (GPCP), and 3) the Climatic Research Unit (CRU), resulting in three slightly different corrected precipitation products. The correction methodology, however, compromises spatial consistency (e.g., storm tracking) on a daily time scale. The effect that the precipitation correction has on the reanalysis-derived global land surface water budgets is investigated by forcing the Variable Infiltration Capacity (VIC) land surface model with all four datasets (i.e., the original reanalysis product and the three corrected datasets). The main components of the land surface water budget cycle are not affected substantially; however, the increased spatial variability in precipitation is reflected in the evaporation and runoff components but reduced in the case of soil moisture. Furthermore, the partitioning of precipitation into canopy evaporation and throughfall is sensitive to the rain day statistics of the correcting dataset, especially in the Tropics, and this has implications for the required accuracy of the correcting dataset. The output fields from these long-term land surface simulations provide a global, consistent dataset of water and energy states and fluxes that can be used for model intercomparisons, studies of annual and seasonal climate variability, and comparisons with current versions of numerical weather prediction models.

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Alan D. Ziegler, Justin Sheffield, Edwin P. Maurer, Bart Nijssen, Eric F. Wood, and Dennis P. Lettenmaier

Abstract

Diagnostic studies of offline, global-scale Variable Infiltration Capacity (VIC) model simulations of terrestrial water budgets and simulations of the climate of the twenty-first century using the parallel climate model (PCM) are used to estimate the time required to detect plausible changes in precipitation (P), evaporation (E), and discharge (Q) if the global water cycle intensifies in response to global warming. Given the annual variability in these continental hydrological cycle components, several decades to perhaps more than a century of observations are needed to detect water cycle changes on the order of magnitude predicted by many global climate model studies simulating global warming scenarios. Global increases in precipitation, evaporation, and runoff of 0.6, 0.4, and 0.2 mm yr−1 require approximately 30–45, 25–35, and 50–60 yr, respectively, to detect with high confidence. These conservative detection time estimates are based on statistical error criteria (α = 0.05, β = 0.10) that are associated with high statistical confidence, 1 − α (accept hypothesis of intensification when true, i.e., intensification is occurring), and high statistical power, 1 − β (reject hypothesis of intensification when false, i.e., intensification is not occurring). If one is willing to accept a higher degree of risk in making a statistical error, the detection time estimates can be reduced substantially. Owing in part to greater variability, detection time of changes in continental P, E, and Q are longer than those for the globe. Similar calculations performed for three Global Energy and Water Experiment (GEWEX) basins reveal that minimum detection time for some of these basins may be longer than that for the corresponding continent as a whole, thereby calling into question the appropriateness of using continental-scale basins alone for rapid detection of changes in continental water cycles. A case is made for implementing networks of small-scale indicator basins, which collectively mimic the variability in continental P, E, and Q, to detect acceleration in the global water cycle.

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Lihong Zhou, Zhenzhong Zeng, Cesar Azorin-Molina, Yi Liu, Jie Wu, Dashan Wang, Dan Li, Alan D. Ziegler, and Li Dong

Abstract

To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980–2018) for 4722 meteorological stations. Through examining monthly averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (−19%), South America (−16%), Australia (−14%), and Asia (−13%), with corresponding rate reductions of −0.13, −0.08, −0.09, and −0.06 m s−1 decade−1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean–atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including the Western Hemisphere warm pool, East Atlantic pattern, Pacific decadal oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind power generation, now and in the future.

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Thomas W. Giambelluca, Dirk Hölscher, Therezinha X. Bastos, Reginaldo R. Frazão, Michael A. Nullet, and Alan D. Ziegler

Abstract

Regional climatic change, including significant reductions in Amazon Basin evaporation and precipitation, has been predicted by numerical simulations of total tropical forest removal. These results have been shown to be very sensitive to the prescription of the albedo shift associated with conversion from forest to a replacement land cover. Modelers have so far chosen to use an “impoverished grassland” scenario to represent the postforest land surface. This choice maximizes the shifts in land surface parameters, especially albedo (fraction of incident shortwave radiation reflected by the surface). Recent surveys show secondary vegetation to be the dominant land cover for some deforested areas of the Amazon. The characteristics of secondary vegetation as well as agricultural land covers other than pasture have received little attention from field scientists in the region. This paper presents the results of field measurements of radiation flux over various deforested surfaces on a small farm in the eastern Amazonian state of Pará. The albedo of fields in active use was as high as 0.176, slightly less than the 0.180 recently determined for Amazonian pasture and substantially less than the 0.19 commonly used in GCM simulations of deforestation. For 10-yr-old secondary vegetation, albedo was 0.135, practically indistinguishable from the recently published mean primary forest albedo of 0.134. Measurements of surface temperature and net radiation show that, despite similarity in albedo, secondary vegetation differs from primary forest in energy and mass exchange. The elevation of midday surface temperature above air temperature was found to be greatest for actively and recently farmed land, declining with time since abandonment. Net radiation was correspondingly lower for fields in active or recent use. Using land cover analyses of the region surrounding the study area for 1984, 1988, and 1991, the pace of change in regional-mean albedo is estimated to have declined and appears to be leveling at a value less than 0.03 above that of the original forest cover.

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Mary C. Barth, Christopher A. Cantrell, William H. Brune, Steven A. Rutledge, James H. Crawford, Heidi Huntrieser, Lawrence D. Carey, Donald MacGorman, Morris Weisman, Kenneth E. Pickering, Eric Bruning, Bruce Anderson, Eric Apel, Michael Biggerstaff, Teresa Campos, Pedro Campuzano-Jost, Ronald Cohen, John Crounse, Douglas A. Day, Glenn Diskin, Frank Flocke, Alan Fried, Charity Garland, Brian Heikes, Shawn Honomichl, Rebecca Hornbrook, L. Gregory Huey, Jose L. Jimenez, Timothy Lang, Michael Lichtenstern, Tomas Mikoviny, Benjamin Nault, Daniel O’Sullivan, Laura L. Pan, Jeff Peischl, Ilana Pollack, Dirk Richter, Daniel Riemer, Thomas Ryerson, Hans Schlager, Jason St. Clair, James Walega, Petter Weibring, Andrew Weinheimer, Paul Wennberg, Armin Wisthaler, Paul J. Wooldridge, and Conrad Ziegler

Abstract

The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.

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