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K. J. Harnos, M. L’Heureux, Q. Ding, and Q. Zhang

Abstract

Previous studies have outlined benefits of using multiple model platforms to make seasonal climate predictions. Here, reforecasts from five models included in the North American Multimodel Ensemble (NMME) project are utilized to determine skill in predicting Arctic sea ice extent (SIE) during 1982–2010. Overall, relative to the individual models, the multimodel average results in generally smaller biases and better correlations for predictions of total SIE and year-to-year (Y2Y), linearly, and quadratically detrended variability. Also notable is the increase in error for NMME predictions of total September SIE during the mid-1990s through 2000s. After 2000, observed September SIE is characterized by more significant negative trends and increased Y2Y variance, which suggests that recent sea ice loss is resulting in larger prediction errors. While this tendency is concerning, due to the possibility of models not accurately representing the changing trends in sea ice, the multimodel approach still shows promise in providing more skillful predictions of Arctic SIE over any individual model.

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Song Yang, X. Ding, D. Zheng, and Q. Li

Abstract

Several advanced analysis tools are applied to depict the time–frequency characteristics of the variations of Great Plains (GP) precipitation and its relationship with tropical central-eastern Pacific Ocean sea surface temperature (SST). These tools are advantageous because they reveal the detailed features of the dominant time scales of precipitation variations, the combined effects of multiscale oscillating signals on the intensity of precipitation, and the variations of SST–precipitation relationships in time and frequency domains. The variability of GP precipitation is characterized by strong annual and semiannual signals, which have the most stable oscillating frequencies and the largest amplitudes. However, nonseasonal signals, which are less oscillatory and have smaller amplitudes and more variable frequencies with time, also contribute significantly to precipitation variability and may modify the seasonal cycle of GP precipitation. The phase of these nonseasonal signals is in phase (out of phase) with that of seasonal signals during the periods of heavy (deficient) precipitation. Significant correlations exist between GP precipitation and Niño-3.4 SST, and the strongest relationship appears when the SST leads the precipitation by 1 month. The GP precipitation increases (decreases) during El Niño (La Niña) episodes. Significant relationships appear on semiannual and annual time scales in the 1950s and on interannual time scales in the 1910s, 1940s, and 1980s. A particularly significant relationship appears on biennial time scales in the 1980s. The revealed SST–precipitation relationship is strongly seasonally dependent, with the greatest significance in summer. Warming of tropical central-eastern Pacific SST weakens the overlying easterly trade winds and strengthens the northward moisture supply from Central America through the Gulf of Mexico to the Great Plains. This dominant SST influence prevails in all seasons. However, the moisture transport from the southwest coast and the Gulf of California also contributes to the variability of GP precipitation in September–November, December–February, and March–May. In June–August, the increase in GP precipitation is caused by convergence between anomalous northerly flow over the northern plains, associated with the warming in the northeastern Pacific, and southerly flow over the southern plains, associated with the warming in the tropical central-eastern Pacific.

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X. Liang, S. Miao, J. Li, R. Bornstein, X. Zhang, Y. Gao, F. Chen, X. Cao, Z. Cheng, C. Clements, W. Dabberdt, A. Ding, D. Ding, J. J. Dou, J. X. Dou, Y. Dou, C. S. B. Grimmond, J. E. González-Cruz, J. He, M. Huang, X. Huang, S. Ju, Q. Li, D. Niyogi, J. Quan, J. Sun, J. Z. Sun, M. Yu, J. Zhang, Y. Zhang, X. Zhao, Z. Zheng, and M. Zhou

Abstract

Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).

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T. J. Bracegirdle, N. A. N. Bertler, A. M. Carleton, Q. Ding, C. J. Fogwill, J. C. Fyfe, H. H. Hellmer, A. Y. Karpechko, K. Kusahara, E. Larour, P. A. Mayewski, W. N. Meier, L. M. Polvani, J. L. Russell, S. L. Stevenson, J. Turner, J. M. van Wessem, W. J. van de Berg, and I. Wainer
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C. L. Reddington, K. S. Carslaw, P. Stier, N. Schutgens, H. Coe, D. Liu, J. Allan, J. Browse, K. J. Pringle, L. A. Lee, M. Yoshioka, J. S. Johnson, L. A. Regayre, D. V. Spracklen, G. W. Mann, A. Clarke, M. Hermann, S. Henning, H. Wex, T. B. Kristensen, W. R. Leaitch, U. Pöschl, D. Rose, M. O. Andreae, J. Schmale, Y. Kondo, N. Oshima, J. P. Schwarz, A. Nenes, B. Anderson, G. C. Roberts, J. R. Snider, C. Leck, P. K. Quinn, X. Chi, A. Ding, J. L. Jimenez, and Q. Zhang

Abstract

The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, to create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.

Open access