edu.ucar.opensky::articles:44010 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:43:54.305256 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d7vd73wk https://doi.org/10.24381/cds.adbb2d47 https Related Dataset #1 ERA5 hourly data on single levels from 1940 to present https://doi.org/10.24381/cds.e2161bac https Related Dataset #2 ERA5-Land hourly data from 1950 to present https://doi.org/10.24381/d58bbf47 https Related Dataset #3 CAMS global reanalysis (EAC4) https://doi.org/10.5067/3Z173KIE2TPD https Related Dataset #4 MERRA-2 inst1_2d_asm_Nx: 2d,3-Hourly,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 https://doi.org/10.5067/7MCPBJ41Y0K6 https Related Dataset #5 MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4 https://doi.org/10.5067/CG4OT8DJX2Z7 https Related Dataset #6 High Mountain Asia 12 km Modeled Estimates of Aerosol Transport, Chemistry, and Deposition Reanalysis, 2003-2019, Version 1 https://doi.org/10.5067/GPM/IMERGDF/DAY/06 https Related Dataset #7 GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06 https://doi.org/10.5067/GPM/IMERGDF/DAY/07 https Related Dataset #8 GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 https://doi.org/10.5067/KLICLTZ8EM9D https Related Dataset #9 MERRA-2 tavg1_2d_aer_Nx: 2d,1-Hourly,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4 https://doi.org/10.5067/LTVB4GPCOTK2 https Related Dataset #10 MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4 https://doi.org/10.5067/MODIS/MCD19A2CMG.061 https Related Dataset #11 MODIS/Terra+Aqua AOD and Water Vapor from MAIAC, Daily L3 Global 0.05Deg CMG V061 https://doi.org/10.5067/MODIS/MOD10C1.061 https Related Dataset #12 MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, Version 61 https://doi.org/10.5067/MODIS/MYD10C1.061 https Related Dataset #13 MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG, Version 61 https://doi.org/10.5067/MODIS/MYD11C1.006 https Related Dataset #14 MYD11C1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006 https://doi.org/10.5067/Q9QMY5PBNV1T https Related Dataset #15 MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4 https://doi.org/10.5067/RKPHT8KC1Y1T https Related Dataset #16 MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4 https://doi.org/10.5067/WWQSXQ8IVFW8 https Related Dataset #17 MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4 https://doi.org/10.7265/4m1f-gd79 https Related Dataset #18 Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 6 Diagnosing aerosol-meteorological interactions on snow within Earth system models: A proof-of-concept study over High Mountain Asia 2025-08-01T00:00:00Z publication https://n2t.net/ark:/85065/d7vd73wk https://doi.org/10.5194/esd-16-1237-2025 Roychoudhury, C. author He, Cenlin author Kumar, Rajesh author Arellano Jr., A. F. author UCAR/NCAR - Library publisher Snowmelt in the Third Pole, or High Mountain Asia (HMA), serves as a vital water source for 30 % of the world's population and is strongly influenced by interactions between aerosols and meteorology. However, understanding these interactions remains uncertain due to their complexity and limitations in existing approaches using model sensitivity and process-denial experiments. In addition, these interactions are insufficiently represented in current climate models. Equally ambiguous is the impact of these interactions on snow processes in the context of climate change. Here we use network theory, a graphical approach that maps the relationships between variables as interconnected nodes, to identify key variables that influence snowmelt processes. We focus on the late snowmelt season (May–July) using daily data (from 2003–2019) from satellite observations and reanalyses. We combine statistical and machine learning methods to highlight the underappreciated relevance of coupled processes between aerosols and meteorology on snow, as well as the inconsistent representation of aerosol–meteorology interactions on snow within major reanalyses. These inconsistencies reflect fundamental differences in model design. In particular, we identify underrepresented dust interactions with near-surface temperature and large-scale circulation and gaps in cloud cover interactions, especially in the least coupled reanalysis. Carbonaceous aerosols and large-scale circulation emerge as main drivers of aerosol–meteorology in snow interactions, highlighting their relevance in Earth system models (ESMs) for the accurate assessment of water availability in developing economies. These insights point to the degree of complexity of these interactions and their relative strength of representation across ESMs. The proposed framework can be extended to help diagnose other complex Earth system processes and complement conventional feedback separation methods. This has broader implications for the future development of coupled models to improve Earth system predictability. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;font-weight:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation