scholarly journals Comparison of optical-equivalent snow grain size estimates under Arctic low Sun conditions during PAMARCMiP 2018

2021 ◽  
Author(s):  
Evelyn Jäkel ◽  
Tim Carlsen ◽  
André Ehrlich ◽  
Manfred Wendisch ◽  
Michael Schäfer ◽  
...  

Abstract. The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Non-hydrostatic – Aerosols and Reactive Trace gases) simulations. The study is focused on low Sun and partly rough surface conditions encountered during a three-week campaign conducted North of Greenland in March/April 2018 within the framework of the PAMARCMiP (Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project) project. Further, we propose an adjusted airborne retrieval method by using the albedo at 1700 nm wavelength. This reduced the effect of atmospheric masking and improved the sensitivity on ropt. From this approach we achieved a significantly improved uncertainty (

2012 ◽  
Vol 140 (10) ◽  
pp. 3259-3277 ◽  
Author(s):  
Chunxi Zhang ◽  
Yuqing Wang ◽  
Axel Lauer ◽  
Kevin Hamilton

Abstract The Weather Research and Forecasting (WRF) model V3.3 has been configured for the Hawaiian Islands as a regional climate model for the region (HRCM). This paper documents the model configuration and presents a preliminary evaluation based on a continuous 1-yr simulation forced by observed boundary conditions with 3-km horizontal grid spacing in the inner nested domain. The simulated vertical structure of the temperature and humidity are compared with twice-daily radiosonde observations at two stations. Generally the trade wind inversion (TWI) height and occurrence days are well represented. The simulation over the islands is compared with observations from nine surface climatological stations and a dense network of precipitation stations. The model simulation has generally small biases in the simulated surface temperature, relative humidity, and wind speed. The model realistically simulated the magnitude and geographical distribution of the mean rainfall over the Hawaiian Islands. In addition, the model simulation reproduced reasonably well the individual heavy rainfall events as seen from the time series of pentad mean rainfall averaged over island scales. Also the model reproduced the geographical variation of the mean diurnal rainfall cycle even though the observed diurnal cycle displays quite different features over different islands. Comparison with results obtained using the land surface dataset from the official release of the WRF model confirmed that the newly implemented land surface dataset generally improved the simulation of surface variables. These results demonstrate that the WRF can be a useful tool for dynamical downscaling of regional climate over the Hawaiian Islands.


2009 ◽  
Vol 48 (10) ◽  
pp. 2152-2159 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
David J. Karoly

Abstract This note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast–type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction–National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis.


2012 ◽  
Vol 13 (2) ◽  
pp. 443-462 ◽  
Author(s):  
Marco Braun ◽  
Daniel Caya ◽  
Anne Frigon ◽  
Michel Slivitzky

Abstract The effect of a regional climate model’s (RCM’s) internal variability (IV) on climate statistics of annual series of hydrological variables is investigated at the scale of 21 eastern Canada watersheds in Quebec and Labrador. The analysis is carried out on 30-yr pairs of simulations (twins), performed with the Canadian Regional Climate Model (CRCM) for present (reanalysis and global climate model driven) and future (global climate model driven) climates. The twins differ only by the starting date of the regional simulation—a standard procedure used to trigger internal variability in RCMs. Two different domain sizes are considered: one comparable to domains used for RCM simulations over Europe and the other comparable to domains used for North America. Results for the larger North American domain indicate that mean relative differences between twin pairs of 30-yr climates reach ±5% when spectral nudging is used. Larger differences are found for extreme annual events, reaching about ±10% for 10% and 90% quantiles (Q10 and Q90). IV is smaller by about one order of magnitude in the smaller domain. Internal variability is unaffected by the period (past versus future climate) and by the type of driving data (reanalysis versus global climate model simulation) but shows a dependence on watershed size. When spectral nudging is deactivated in the large domain, the relative difference between pairs of 30-yr climate means almost doubles and approaches the magnitude of a global climate model’s internal variability. This IV at the level of the natural climate variability has a profound impact on the interpretation, analysis, and validation of RCM simulations over large domains.


2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Gábor Mezősi ◽  
Viktória Blanka ◽  
Teodóra Bata ◽  
Zsuzsanna Ladányi ◽  
Gábor Kemény ◽  
...  

AbstractThe changes in rate and pattern of wind erosion sensitivity due to climate change were investigated for 2021–2050 and 2071–2100 compared to the reference period (1961–1990) in Hungary. The sensitivities of the main influencing factors (soil texture, vegetation cover and climate factor) were evaluated by fuzzy method and a combined wind erosion sensitivity map was compiled. The climate factor, as the driving factor of the changes, was assessed based on observed data for the reference period, while REMO and ALADIN regional climate model simulation data for the future periods. The changes in wind erosion sensitivity were evaluated on potentially affected agricultural land use types, and hot spot areas were allocated. Based on the results, 5–6% of the total agricultural areas were high sensitive areas in the reference period. In the 21st century slight or moderate changes of wind erosion sensitivity can be expected, and mostly ‘pastures’, ‘complex cultivation patterns’, and ‘land principally occupied by agriculture with significant areas of natural vegetation’ are affected. The applied combination of multi-indicator approach and fuzzy analysis provides novelty in the field of land sensitivity assessment. The method is suitable for regional scale analysis of wind erosion sensitivity changes and supports regional planning by allocating priority areas where changes in agro-technics or land use have to be considered.


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