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Jason P. Giovannettone and Ana P. Barros

as a “continental” orographic monsoon regime because of the affect of the large landmass that separates the mountains from the Bay of Bengal to the east and the Arabian Sea to the west ( Lang and Barros 2002 ). In Mexico, however, the mountains are aligned along the coast, with minimal land–atmosphere interactions with approaching air masses prior to reaching the mountains, and thus it can be described as a “maritime” orographic regime, in the sense that rainfall events are directly dependent on

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Jun Wen, Xin Wang, and Kun Yang

unrealistically high values for the unfrozen/liquid soil water content at very low temperatures, a limit is set on parameter b in Eq. (4) and Eq. (6) [or Eq. (7) ] when estimating ice content: where b l is an empirical parameter taken as 5.5. b. Field site and measurements The Maqu station is located in the source region of the Yellow River (SRYR) over the northeastern part of the Tibetan Plateau ( Fig. 1 ), with elevations varying from 3100 to 4300 m above mean sea level. The weather is

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Alison M. Anders and Stephen W. Nesbitt

, precipitation is dominated by convective processes, making the stable upslope model inappropriate. Furthermore, controls on convective precipitation beyond elevation indicate that a single conceptual model may not adequately describe precipitation–elevation relationships across the tropics. Altitudinal gradients in precipitation across the tropics are variable, including regimes with near–sea level maxima, regimes with moderate-elevation precipitation maxima, and those with approximately uniform

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Vladimir V. Smirnov and G. W. K. Moore

basin ( Cao et al. 2001 ). This makes the study of the atmospheric water vapor balance in this region particularly important. Furthermore, the Mackenzie is the fourth largest river of the Arctic Ocean Basin. Its discharge, along with that of the other north flowing rivers, plays an important role in controlling the production of sea ice in the Arctic Ocean ( Cattle 1985 ; Manak and Mysak 1989 ) and in regulating deep water formation in its marginal seas ( Aagaard and Carmack 1989 ). The latter

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Rebecca J. Ross, William P. Elliott, Dian J. Seidel, and Participating AMIP-II Modeling Groups

forcings (e.g., sea surface temperature, sea ice, greenhouse gas concentrations, solar radiation), which were based on observational data. Although about 35 models are expected to produce simulations for AMIP II, not all simulations are available yet. We have used the 12 available simulations from models listed in Table 1 . Each model grid point was treated in the same manner as the radiosonde station data. Monthly, seasonal, and annual anomalies of T, RH, and q were computed for the 850-, 700

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Nikolaos S. Bartsotas, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Stavros Solomos, and George Kallos

version of RAMS, version 6.0 ( Pielke et al. 1992 ; Cotton et al. 2003 ). RAMS–ICLAMS is particularly suitable for high-resolution simulations of clouds and precipitation, as it includes a detailed two-moment (mass and number) bulk microphysical scheme ( Meyers et al. 1997 ) describing the in-cloud processes for seven categories of hydrometeors (cloud droplets, rain droplets, pristine ice, snow, aggregates, graupel, and hail). Natural emissions (mineral dust and sea salt) are included in the model as

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Xianghui Kong, Aihui Wang, Xunqiang Bi, Xingyu Li, and He Zhang

are given in Table 2 . Over land, the lower boundary conditions in CAS-ESM and CESM are represented by the same land surface component, the Community Land Surface Model, version 4.5 (CLM4.5; Oleson et al. 2013 ). The AMIP simulations use the observed monthly sea surface temperatures (SST) and sea ice as the boundary conditions ( Gates 1992 ). Monthly SST and sea ice from the Hadley Center SST ( Rayner et al. 2003 ), as well as weekly SST data from the National Oceanic and Atmospheric

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Chul-Su Shin, Bohua Huang, Paul A. Dirmeyer, Subhadeep Halder, and Arun Kumar

, such as land–atmosphere feedbacks arising from the memory in land surface states, may still be underrealized ( Roundy and Wood 2015 ; Dirmeyer and Halder 2016 ). Recently, we conducted two sets of reforecasts initialized with two different land analyses for the period of 1979–2010 ( section 2 ; Shin et al. 2020 ). Since atmosphere, ocean, and sea ice initial states are identical for both sets of reforecasts, this identical-twin set of reforecasts isolates the effect of the uncertainty of the land

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Stefan Hagemann, Cui Chen, Jan O. Haerter, Jens Heinke, Dieter Gerten, and Claudio Piani

.1 for the ocean ( Madec et al. 1998 ), and GELATO 2 for sea ice ( Salas-Mélia 2002 ). The distributions of marine, desert, urban aerosols, and sulfate aerosols were specified, whereas for aerosols only the direct effect of anthropogenic sulfate aerosols was taken into account. 3) IPSL The L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4; hereafter simply IPSL) includes the submodels LMDZ-4 for the atmosphere ( Hourdin et al. 2006 ), ORCA for the ocean (based on the OPA model

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

instruments, limiting rainfall signals to an indirect, nonunique relationship between cloud ice-scattering signatures and surface rainfall. Based on the mean observed ratio between ice aloft and the surface rainfall, these estimates can often be inaccurate, with more pronounced biases observed during extreme events. In addition to the example given in study by Petković and Kummerow (2015) , a difference in mean precipitation rate bias between ground radar measurements and an operational satellite PMW

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