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In this study, we calibrate a regional climate model’s (RCM) underlying land surface model (LSM). In addition to providing a realistic representation of runoff across the hydroclimatically diverse western United States, this is done to take advantage of the RCM’s ability to physically resolve meteorological forcing data in ungauged regions, and to prepare the calibrated hydrologic model for tight coupling, or the ability to represent land surface–atmosphere interactions, with the RCM. Specifically, we use a 9-km resolution meteorological forcing dataset across the western United States, from the fifth generation ECMWF Reanalysis (ERA5) downscaled by the Weather Research Forecasting (WRF) regional climate model, as an offline forcing for Noah-Multiparameterization (Noah-MP). We detail the steps involved in producing an LSM capable of accurately representing runoff, including physical parameterization selection, parameter calibration, and regionalization to ungauged basins. Based on our model evaluation from 1954 to 2021 for 586 basins with daily natural streamflow, the streamflow bias is reduced from 24.2% to 4.4%, and the median daily Nash–Sutcliffe efficiency (NSE) is improved from 0.12 to 0.36. When validating against basins with monthly natural streamflow data, we obtain a similar reduction in bias and a median monthly NSE improvement from 0.18 to 0.56. In this study, we also discover the optimal setup when using a donor-basin method to regionalize parameters to ungauged basins, which can vary by 0.06 NSE for unique designs of this regionalization method. Significance Statement This study provides useful guidance for improving a land surface model to accurately represent runoff across a spatially extensive and hydroclimatically diverse region (the western United States). The land surface model is updated to represent runoff more accurately at gauged basins, and then additionally updated for basins without observational data using a mathematical approach called the donor-basin method. We make use of a regional climate model’s reanalysis-derived meteorological data and its underlying land surface model to achieve realistic runoff. The calibrated land surface model can thus be tightly coupled in subsequent studies in a manner that should more accurately reflect runoff conditions. Findings from this study will serve as a useful reference for the atmospheric (and hydrologic) modeling communities and their ability to represent large-scale hydrology accurately.

How do ocean initial states impact historical and future climate projections in Earth system models? To answer this question, we use the 50-member Canadian Earth System Model (CanESM2) large ensemble, in which individual ensemble members are initialized using a combination of different oceanic initial states and atmospheric microperturbations. We show that global ocean heat content anomalies associated with the different ocean initial states, particularly differences in deep ocean heat content due to ocean drift, persist from initialization at year 1950 through the end of the simulations at year 2100. We also find that these anomalies most readily impact surface climate over the Southern Ocean. Differences in ocean initial states affect Southern Ocean surface climate because persistent deep ocean temperature anomalies upwell along sloping isopycnal surfaces that delineate neighboring branches of the upper and lower cells of the global meridional overturning circulation. As a result, up to a quarter of the ensemble variance in Southern Ocean turbulent heat fluxes, heat uptake, and surface temperature trends can be traced to variance in the ocean initial state, notably deep ocean temperature differences of order 0.1 K due to model drift. Such a discernible impact of varying ocean initial conditions on ensemble variance over the Southern Ocean is evident throughout the full 150 simulation years of the ensemble, even though upper ocean temperature anomalies due to varying ocean initial conditions rapidly dissipate over the first two decades of model integration over much of the rest of the globe.

How do ocean initial conditions impact historical and future climate projections in Earth system models? To answer this question, we use the 50-member Canadian Earth System Model (CanESM2) large ensemble, in which individual ensemble members are initialized using a strategic combination of different oceanic initial states and different atmospheric perturbations. We show that global ocean heat content anomalies associated with the different ocean initial states persist from initialization at year 1950 through the end of the simulations at year 2100. We also find that these anomalies most readily impact surface climate over the Southern Ocean. Ocean initial conditions affect Southern Ocean surface climate because persistent deep ocean temperature anomalies upwell along sloping isopycnal surfaces that delineate neighboring branches of the Upper and Lower Cells of the Global Meridional Overturning Circulation. As a result, up to a quarter of the ensemble variance in Southern Ocean turbulent heat fluxes, heat uptake, and surface temperature trends can be traced to variance in the ocean initial state. Such a discernible impact of varying ocean initial conditions on ensemble variance over the Southern Ocean is evident throughout the full 150 simulation years of the ensemble, even though upper ocean temperature anomalies due to varying ocean initial conditions rapidly dissipate over the first two decades of model integration over much of the rest of the globe.

Author(s): Rutz, JJ; Shields, CA; Lora, JM; Payne, AE; Guan, B; Ullrich, P; O’Brien, T; Leung, LR; Ralph, FM; Wehner, M; Brands, S; Collow, A; Goldenson, N; Gorodetskaya, I; Griffith, H; Kashinath, K; Kawzenuk, B; Krishnan, H; Kurlin, V; Lavers, D; Magnusdottir, G; Mahoney, K; McClenny, E; Muszynski, G; Nguyen, PD; Prabhat, M; Qian, Y; Ramos, AM; Sarangi, C; Sellars, S; Shulgina, T; Tome, R; Waliser, D; Walton, D; Wick, G; Wilson, AM; Viale, M | Abstract: Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

Author(s): Shields, Christine A; Rutz, Jonathan J; Leung, Lai-Yung; Ralph, F Martin; Wehner, Michael; Kawzenuk, Brian; Lora, Juan M; McClenny, Elizabeth; Osborne, Tashiana; Payne, Ashley E; Ullrich, Paul; Gershunov, Alexander; Goldenson, Naomi; Guan, Bin; Qian, Yun; Ramos, Alexandre M; Sarangi, Chandan; Sellars, Scott; Gorodetskaya, Irina; Kashinath, Karthik; Kurlin, Vitaliy; Mahoney, Kelly; Muszynski, Grzegorz; Pierce, Roger; Subramanian, Aneesh C; Tome, Ricardo; Waliser, Duane; Walton, Daniel; Wick, Gary; Wilson, Anna; Lavers, David; Collow, Allison; Krishnan, Harinarayan; Magnusdottir, Gudrun; Nguyen, Phu

We present the results of a HST WFPC2 search for substellar companions to members of the star-forming cluster IC 348. The detection limits for companions to low-mass stars and brown dwarfs in the PC images are dm_791=0, 2.5, and 5.5 at separations of 0.05", 0.1", and 0.3", respectively, which correspond to M_2/M_1=1, 0.3, and 0.1 at 15, 30, and 90 AU. Meanwhile, for heavily saturated solar-mass primaries in the WFC images, the limits are dm_791=0 and 6 (M_2/M_1=1 and 0.04) at 0.2" and 0.4". The sky limiting magnitude of m_791~26 at large separations from a primary corresponds to a mass of ~0.006 M_sun. For the two faintest candidate companions appearing in these data, we have obtained 0.8-2.5 um spectra with SpeX at the IRTF, which we classify as cluster members with spectral types near M6, corresponding to masses of ~0.1 M_sun. Thus, no probable substellar companions are detected in this survey. The frequencies of stellar and substellar companions within 0.4-5" (120-1600 AU) from low-mass stars in IC 348 agree with measurements in the field. The factor of ~3-10 deficiency in brown dwarfs relative to stars among companions at wide separations in IC 348 and across the much larger range of separations probed for field stars is equal within the uncertainties to the deficiency in brown dwarfs in measurements of mass functions of isolated objects. In other words, when defined relative to stars, the brown dwarf "desert" among companions is also present among isolated objects, which is expected if stellar and substellar companions form in the same manner as their free-floating counterparts. Meanwhile, among the 14 substellar primaries in our survey of IC 348, no companions are detected. This absence of wide binary brown dwarfs is statistically consistent with the frequency of wide binary stars in IC 348.

The presence of light-absorbing aerosol particles deposited on arctic snow and sea ice influences the surface albedo, causing greater shortwave absorption, warming, and loss of snow and sea ice, lowering the albedo further. The Community Earth System Model version 1 (CESM1) now includes the radiative effects of light-absorbing particles in snow on land and sea ice and in sea ice itself. We investigate the model response to the deposition of black carbon and dust to both snow and sea ice. For these purposes we employ a slab ocean version of CESM1, using the Community Atmosphere Model version 4 (CAM4), run to equilibrium for year 2000 levels of CO2 and fixed aerosol deposition. We construct experiments with and without aerosol deposition, with dust or black carbon deposition alone, and with varying quantities of black carbon and dust to approximate year 1850 and 2000 deposition fluxes. The year 2000 deposition fluxes of both dust and black carbon cause 1–2 °C of surface warming over large areas of the Arctic Ocean and sub-Arctic seas in autumn and winter and in patches of Northern land in every season. Atmospheric circulation changes are a key component of the surface-warming pattern. Arctic sea ice thins by on average about 30 cm. Simulations with year 1850 aerosol deposition are not substantially different from those with year 2000 deposition, given constant levels of CO2. The climatic impact of particulate impurities deposited over land exceeds that of particles deposited over sea ice. Even the surface warming over the sea ice and sea ice thinning depends more upon light-absorbing particles deposited over land. For CO2 doubled relative to year 2000 levels, the climate impact of particulate impurities in snow and sea ice is substantially lower than for the year 2000 equilibrium simulation.

[1] A radiative-conductive balance calculation is presented to predict dust temperatures in the Mars atmosphere and to evaluate the assumption that dust and gas in the atmosphere are in thermal equilibrium. Mie theory is used to calculate the absorption and scattering properties of dust, important for the heating of dust by sunlight; scattering is neglected in the treatment of infrared absorption and emission by grains. Besides radiating in the infrared, dust grains cool via collisions with gas molecules, at a rate proportional to the gas density. We find that dust grains are in thermal equilibrium with the atmospheric gas near the surface. At a predictable pressure, which depends on grain size, the dust and gas temperatures decouple, as radiative absorption and emission increasingly dominate over exchange of thermal energy due to collisions with gas molecules. This non-equilibrium between dust and gas will affect calculations of heating in the atmosphere.

We present experimental measurements of a granular slope under horizontal vibration. We use optical particle tracking to measure the motion of surface beads as the slope fails. We find that for all but the largest inclination angles, initial bead motion leads to strengthening rather than an avalanche. The initial motion of the beads is usually intermittent and evolves differently for different preparations, slope angles, and rates of increase in the vibration amplitude. When a specific criterion is chosen to define failure, the Coulomb friction model adequately describes the average acceleration required to produce failure, as long as slope preparation and experimental protocol are constant. However, the observed intermittent motion and rate dependence indicate that strengthening microrearrangements are important features that affect failure of slopes under external perturbations.