Downscaling of forcing data Temperature, Shortwave (Solar) & Longwave (Thermal) CHARIS meeting, Dehra Dun, India, October 2014 Presented by: Karl Rittger

Spatial variability in forcing variables Temperature and precip. gradients North and south facing slopes Kanjin, Langtang Himal, November 2013 Along valley gradients 2CHARIS meeting, Dehra Dun, India, October 2014

Snow heterogeneity Wind redistribution (D. Marks) Differential ablation (E. Bair) 3CHARIS meeting, Dehra Dun, India, October 2014

Meteorological stations Sparse coverage Few high elevation stations Possibly short series How representative are they Need to be “upscaled”/extrapolated Needs knowledge of lapse rates for P and T. 4CHARIS meeting, Dehra Dun, India, October 2014

Snow cover from Landsat 5 & in-situ data 5 July 02 Fractional snow cover July 18 Fractional snow cover Automated SWE observations Streamflow observations CHARIS meeting, Dehra Dun, India, October 2014

Climate reanalysis 6 Numerical description of the recent climate Combines models of physical processes in the atmosphere and oceans with observations Observations Air temperature, pressure, wind Rainfall, soil moisture, sea-surface temp Simplified topography May be biased Needs to be downscaled Globally complete Long time series consistent CHARIS meeting, Dehra Dun, India, October 2014

Atmospheric reanalysis used in NASA’s Land Data Assimilation System (LDAS) 7 GLDAS-1: Current Higher resolution GLDAS-2: Long time scale Consistent CHARIS meeting, Dehra Dun, India, October 2014

Atmospheric reanalysis used in NASA’s Land Data Assimilation System (LDAS) 8 Drives multiple land surface models from simple to complex CHARIS meeting, Dehra Dun, India, October 2014

Downscaling of forcing data Temperature  Based on lapse rates and elevation differences between coarse and fine DEMs Shortwave and Longwave  Scale (time and space)  Elevation  Topography  Forest 9CHARIS meeting, Dehra Dun, India, October 2014

Downscaling of forcing data Temperature  Based on lapse rates and elevation differences between coarse and fine DEMs Shortwave and Longwave  Scale (time and space)  Elevation  Topography  Forest 10CHARIS meeting, Dehra Dun, India, October 2014

GLDAS-1-NOAH: air temperature 11CHARIS meeting, Dehra Dun, India, October 2014

Sierra Nevada, California (Elevation) m (1/8° or about 14km) CHARIS meeting, Dehra Dun, India, October 2014

Sierra Nevada, California (Elevation) m scale (1/8° or about 14km) CHARIS meeting, Dehra Dun, India, October 2014

Downscaling temperature using a lapse rate and change in elevation 14CHARIS meeting, Dehra Dun, India, October 2014 Air Temperature,

Air Temperature (T a ), (Video) 15CHARIS meeting, Dehra Dun, India, October 2014

But environmental lapse rates change through time and from place to place. Reanalysis data has air temperatures for many levels in the atmosphere Atmospheric profile can be used to estimate lapse rate. A more rigorous approach to the lapse rate, γ 16 Simplest approach is to use a standard lapse rate to disaggregate model temperature to finer grid. CHARIS meeting, Dehra Dun, India, October 2014

Downscaling of forcing data Temperature  Based on lapse rates and elevation differences between coarse and fine DEMs Shortwave and Longwave  Scale (time and space)  Elevation  Topography  Forest 17CHARIS meeting, Dehra Dun, India, October 2014

GLDAS: incoming solar 18CHARIS meeting, Dehra Dun, India, October 2014

Net Shortwave Radiation: Tuolumne-Merced River basins March 1, :00 Painter et al., 2009; Dozier et al., 2008 Link and Marks, 1999; Garren and Marks, 2005 Dozier and Frew, 1990 Erbs et al., 1982; Olyphant et al., 1984 Dubayah and Loechel., 1997 Cosgrove et al., 2003; Pinker et al., 2003; Mitchell et al., CHARIS meeting, Dehra Dun, India, October 2014

GLDAS: incoming thermal 20CHARIS meeting, Dehra Dun, India, October 2014

Net Longwave Radiation: Tuolumne-Merced River basins March 1, :00 Dozier and Frew, 1990 Link and Marks, 1999; Garren and Marks, 2005 Dubayah and Loechel, CHARIS meeting, Dehra Dun, India, October 2014

Summary Need to capture processes at small scale in modeling snow accumulation and melt Global re-analysis data available from several sources Coupled with land surface models to provide surface fluxes Downscaling based on  Elevation, slope, aspect, solar angles, vegetation, shading, dynamic lapse rates 22CHARIS meeting, Dehra Dun, India, October 2014

ERA-Interim estimates lapse rates 23CHARIS meeting, Dehra Dun, India, October 2014