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conditions, reanalysis products also provide estimates of land surface fields, including surface meteorological forcing data (such as precipitation, radiation, air temperature, and humidity) as well as land surface states and fluxes (such as soil moisture, snow, and runoff). Reanalysis estimates can be used for a large variety of research and applications, for example, the generation of enhanced land surface meteorological datasets ( Berg et al. 2005 ; Guo et al. 2006 ; Sheffield et al. 2006 ), the
conditions, reanalysis products also provide estimates of land surface fields, including surface meteorological forcing data (such as precipitation, radiation, air temperature, and humidity) as well as land surface states and fluxes (such as soil moisture, snow, and runoff). Reanalysis estimates can be used for a large variety of research and applications, for example, the generation of enhanced land surface meteorological datasets ( Berg et al. 2005 ; Guo et al. 2006 ; Sheffield et al. 2006 ), the
1. Introduction Reanalysis products are typically used for many varying applications in the earth science community due to the lack of globally and temporally complete direct observations ( Qian et al. 2006 ). Examples of the uses of reanalysis products are to drive land surface models, study the climate system, and provide boundary conditions for regional modeling. Reanalysis products merge available observations with a state-of-the-art atmospheric (or more recently, coupled ocean
1. Introduction Reanalysis products are typically used for many varying applications in the earth science community due to the lack of globally and temporally complete direct observations ( Qian et al. 2006 ). Examples of the uses of reanalysis products are to drive land surface models, study the climate system, and provide boundary conditions for regional modeling. Reanalysis products merge available observations with a state-of-the-art atmospheric (or more recently, coupled ocean
NRMSE MERRA,RSWCLR ( Fig. 2e ). The RSW DC intermodel differences in the land nonconvective regions are caused by errors in RSW CLR,DC due to an error in the climatological annual diurnal evolution of clear-sky albedo (not shown). b. Attribution of climatological diurnal cycle errors The previous section outlined errors in the TOA flux diurnal cycle representation within MERRA and ERA-Interim. This section attributes the NRMSE values to specific diurnal cycle characteristics—magnitude and timing—using
NRMSE MERRA,RSWCLR ( Fig. 2e ). The RSW DC intermodel differences in the land nonconvective regions are caused by errors in RSW CLR,DC due to an error in the climatological annual diurnal evolution of clear-sky albedo (not shown). b. Attribution of climatological diurnal cycle errors The previous section outlined errors in the TOA flux diurnal cycle representation within MERRA and ERA-Interim. This section attributes the NRMSE values to specific diurnal cycle characteristics—magnitude and timing—using
study focuses on global land areas with additional emphasis on northern high-latitude regions (>45°N), where terrestrial carbon, water, and energy fluxes provide potentially important climate feedbacks and modeling efforts rely heavily on global reanalysis data. 2. Data The datasets and in situ observations used for evaluation and validation of the MERRA land parameters in this study are summarized in Table 1 . We evaluated GEOS-4 and MERRA surface meteorological data against AMSR-E [University of
study focuses on global land areas with additional emphasis on northern high-latitude regions (>45°N), where terrestrial carbon, water, and energy fluxes provide potentially important climate feedbacks and modeling efforts rely heavily on global reanalysis data. 2. Data The datasets and in situ observations used for evaluation and validation of the MERRA land parameters in this study are summarized in Table 1 . We evaluated GEOS-4 and MERRA surface meteorological data against AMSR-E [University of
meteorological forcing fields and surface fluxes over land from MERRA and other reanalyses with satellite estimates and in situ observations from flux towers. Roberts et al. (2011, manuscript submitted to J. Climate ) and Brunke et al. (2011) analyze surface turbulent fluxes over the ocean from MERRA and other data products. Harnik et al. (2011) use MERRA to analyze decadal changes in downward wave coupling between the stratosphere and troposphere. By identifying both the strengths and weaknesses of the
meteorological forcing fields and surface fluxes over land from MERRA and other reanalyses with satellite estimates and in situ observations from flux towers. Roberts et al. (2011, manuscript submitted to J. Climate ) and Brunke et al. (2011) analyze surface turbulent fluxes over the ocean from MERRA and other data products. Harnik et al. (2011) use MERRA to analyze decadal changes in downward wave coupling between the stratosphere and troposphere. By identifying both the strengths and weaknesses of the
surface downward shortwave radiation is overestimated. Table 1. Energy fluxes (March 2000–May 2004) partitioned by global, land, and ocean averages comparable to the estimates developed by TFK09 (their Table 2). TFK09 also provide ISSCP-FD, NCEP reanalyses, JRA-25, and WHOI and HOAPS ocean fluxes. The mean annual cycle is computed first, then the annual average is computed. Since MERRA and the reanalyses considered in TFK09 use prescribed SST, the ocean temperatures and heat content do not
surface downward shortwave radiation is overestimated. Table 1. Energy fluxes (March 2000–May 2004) partitioned by global, land, and ocean averages comparable to the estimates developed by TFK09 (their Table 2). TFK09 also provide ISSCP-FD, NCEP reanalyses, JRA-25, and WHOI and HOAPS ocean fluxes. The mean annual cycle is computed first, then the annual average is computed. Since MERRA and the reanalyses considered in TFK09 use prescribed SST, the ocean temperatures and heat content do not
intensity at link frequencies around 35 GHz. The proximity of the measurements to the ground is an additional advantage with respect to other remote sensing techniques, such as weather radar (e.g., Berne et al. 2004a ). The availability of dense networks of such links used for cellular communication ( Messer et al. 2006 ; Leijnse et al. 2007b ; Zinevich et al. 2008 , 2009 ) over large portions of the earth’s land surface could potentially be used to greatly improve global rainfall estimation. The
intensity at link frequencies around 35 GHz. The proximity of the measurements to the ground is an additional advantage with respect to other remote sensing techniques, such as weather radar (e.g., Berne et al. 2004a ). The availability of dense networks of such links used for cellular communication ( Messer et al. 2006 ; Leijnse et al. 2007b ; Zinevich et al. 2008 , 2009 ) over large portions of the earth’s land surface could potentially be used to greatly improve global rainfall estimation. The
of NCEP reanalyses ( Saha et al. 2010 ). It uses the Climate Forecast System (CFS), a fully coupled atmosphere–ocean–sea ice–land model. The atmospheric component is run at a spectral resolution of T382 with 64 vertical layers with the addition of a cloud microphysics scheme to determine cloud condensate prognostically ( Zhao and Carr 1997 ; Sundqvist et al. 1989 ; Moorthi et al. 2001 ), the simplified Arakawa–Schubert cumulus convection scheme ( Pan and Wu 1995 ; Hong and Pan 1998 ), and
of NCEP reanalyses ( Saha et al. 2010 ). It uses the Climate Forecast System (CFS), a fully coupled atmosphere–ocean–sea ice–land model. The atmospheric component is run at a spectral resolution of T382 with 64 vertical layers with the addition of a cloud microphysics scheme to determine cloud condensate prognostically ( Zhao and Carr 1997 ; Sundqvist et al. 1989 ; Moorthi et al. 2001 ), the simplified Arakawa–Schubert cumulus convection scheme ( Pan and Wu 1995 ; Hong and Pan 1998 ), and
are small and unremarkable when averaged over global domains, but in the following section we will note significant regional variability. For averages taken over ocean and land areas at the global scale the trend and nonstationary annual cycle signals appear to completely dominate any physical variations that might be present on interannual and longer time scales. b. EOF analysis For convenience as a diagnostic, we have also used principal component analysis (PCA) to extract major patterns of
are small and unremarkable when averaged over global domains, but in the following section we will note significant regional variability. For averages taken over ocean and land areas at the global scale the trend and nonstationary annual cycle signals appear to completely dominate any physical variations that might be present on interannual and longer time scales. b. EOF analysis For convenience as a diagnostic, we have also used principal component analysis (PCA) to extract major patterns of
atmosphere–ocean–sea ice–land model for its short-term forecasts. The precipitation ( P ) and surface evaporation/sublimation ( E ) data are taken from the reanalyses forecast fields. The E is not available in NCEP-2 and JRA-25 and is calculated (over Antarctica) from the latent heat fluxes at the surface using a constant latent heat of sublimation of 2.838 × 10 6 J kg −1 , after Rogers and Yau (1989) . b. Antarctic SMB and other methodological aspects Over Antarctica, precipitation minus surface
atmosphere–ocean–sea ice–land model for its short-term forecasts. The precipitation ( P ) and surface evaporation/sublimation ( E ) data are taken from the reanalyses forecast fields. The E is not available in NCEP-2 and JRA-25 and is calculated (over Antarctica) from the latent heat fluxes at the surface using a constant latent heat of sublimation of 2.838 × 10 6 J kg −1 , after Rogers and Yau (1989) . b. Antarctic SMB and other methodological aspects Over Antarctica, precipitation minus surface
