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.625° regular latitude–longitude grid for publication. Note that the longitudinal resolution of the outputs is slightly different between MERRA-2 (0.625°) and MERRA/MERRA-Land (0.667°). Estimates of surface meteorological and land surface fields are available at hourly time steps. The specific MERRA-2 data used here include monthly total land water storage, snow mass, snow cover fraction (SCF), runoff, evaporation, and precipitation ( GMAO 2015a ); hourly soil moisture, snow mass, snow depth, and snow cover
.625° regular latitude–longitude grid for publication. Note that the longitudinal resolution of the outputs is slightly different between MERRA-2 (0.625°) and MERRA/MERRA-Land (0.667°). Estimates of surface meteorological and land surface fields are available at hourly time steps. The specific MERRA-2 data used here include monthly total land water storage, snow mass, snow cover fraction (SCF), runoff, evaporation, and precipitation ( GMAO 2015a ); hourly soil moisture, snow mass, snow depth, and snow cover
its high, perpetually snow-covered interior regions are extremely different from other land areas. Finally, the MERRA datasets were evaluated by comparison to the EBAF data, as described below. 3. Results a. Long-term mean global energy budget We begin by examining the long-term global mean energy budget in MERRA, MERRA-2, and EBAF. Averages of each of the radiative flux components over the time period January 2001–December 2015 are listed in Table 2 (for the TOA) and Table 3 (at the surface
its high, perpetually snow-covered interior regions are extremely different from other land areas. Finally, the MERRA datasets were evaluated by comparison to the EBAF data, as described below. 3. Results a. Long-term mean global energy budget We begin by examining the long-term global mean energy budget in MERRA, MERRA-2, and EBAF. Averages of each of the radiative flux components over the time period January 2001–December 2015 are listed in Table 2 (for the TOA) and Table 3 (at the surface
Records for Use in Research Environments (MEaSUREs) program daily surface melt product ( Mote and Anderson 1995 ; Mote 2007 ) covers the full ice sheet with a grid spacing of 25 km. These data have been used to document the recent, enhanced melt over the last decade, including the 11 July 2012 event in which nearly the entirety of the GrIS experienced melt conditions. It is of interest to understand the conditions and processes that are associated with enhanced melt events that are documented in
Records for Use in Research Environments (MEaSUREs) program daily surface melt product ( Mote and Anderson 1995 ; Mote 2007 ) covers the full ice sheet with a grid spacing of 25 km. These data have been used to document the recent, enhanced melt over the last decade, including the 11 July 2012 event in which nearly the entirety of the GrIS experienced melt conditions. It is of interest to understand the conditions and processes that are associated with enhanced melt events that are documented in
the use of observed precipitation over land, which can be thought of as a poor-man’s soil moisture and snow analysis, MERRA-2 does not assimilate land surface observations. MERRA-2 covers the period from 1980 to the present and continues to be updated with a latency on the order of weeks. The AGCM is run on a cube-sphere grid with an approximate resolution of 50 km × 50 km, the atmospheric analysis operates on a Gaussian grid of the same resolution, and the output fields are interpolated to a 0
the use of observed precipitation over land, which can be thought of as a poor-man’s soil moisture and snow analysis, MERRA-2 does not assimilate land surface observations. MERRA-2 covers the period from 1980 to the present and continues to be updated with a latency on the order of weeks. The AGCM is run on a cube-sphere grid with an approximate resolution of 50 km × 50 km, the atmospheric analysis operates on a Gaussian grid of the same resolution, and the output fields are interpolated to a 0
, while the snow depths are updated using satellite- and ground-based snow-cover and snow-depth observations. b. Annual global land energy budget estimates We compare the reanalyses’ annual global land energy budgets to three state-of-the-art estimates, from Trenberth et al. (2009) , Wild et al. (2015) , and the NEWS program estimates of L’Ecuyer et al. (2015) . Each of these is based on a weighted merger of multiple modeled and observed datasets, and each applies to the energy budget at the start
, while the snow depths are updated using satellite- and ground-based snow-cover and snow-depth observations. b. Annual global land energy budget estimates We compare the reanalyses’ annual global land energy budgets to three state-of-the-art estimates, from Trenberth et al. (2009) , Wild et al. (2015) , and the NEWS program estimates of L’Ecuyer et al. (2015) . Each of these is based on a weighted merger of multiple modeled and observed datasets, and each applies to the energy budget at the start
, as well as the energy and hydrologic properties of an overlying, variable snow cover. Snow hydrology follows a modified version of the Stieglitz model that is also used over terrestrial land surfaces ( Lynch-Stieglitz 1994 ; Stieglitz et al. 2001 ). This provides an explicit representation of snow densification, meltwater runoff, percolation, refreezing, and a prognostic surface albedo based on Greuell and Konzelmann (1994) . Figure 25 shows the effects of the different surface configurations
, as well as the energy and hydrologic properties of an overlying, variable snow cover. Snow hydrology follows a modified version of the Stieglitz model that is also used over terrestrial land surfaces ( Lynch-Stieglitz 1994 ; Stieglitz et al. 2001 ). This provides an explicit representation of snow densification, meltwater runoff, percolation, refreezing, and a prognostic surface albedo based on Greuell and Konzelmann (1994) . Figure 25 shows the effects of the different surface configurations
the exception of volcanic SO 2 emissions, when a given emissions inventory ends the final emission year is persisted in the model. Natural emissions of SO 2 from volcanoes derive from the AeroCom Phase II project ( Diehl et al. 2012 ; http://aerocom.met.no/ ) and cover eruptive and degassing volcanoes on all days from 1 January 1979 to 31 December 2010. Only a repeating annual cycle of degassing volcanoes is included in MERRA-2 after 2010. Eruptive volcanoes emit in the upper third of the
the exception of volcanic SO 2 emissions, when a given emissions inventory ends the final emission year is persisted in the model. Natural emissions of SO 2 from volcanoes derive from the AeroCom Phase II project ( Diehl et al. 2012 ; http://aerocom.met.no/ ) and cover eruptive and degassing volcanoes on all days from 1 January 1979 to 31 December 2010. Only a repeating annual cycle of degassing volcanoes is included in MERRA-2 after 2010. Eruptive volcanoes emit in the upper third of the
al. 2016 ; Huang et al. 2016 ). The two other events occurred in 1982/83 and 1997/98. Sea surface temperature (SST) anomalies for the established ENSO indicator regions show that although the 2015/16 El Niño showed the largest anomalies over the Niño-3.4 region (in late 2015) that partly covers the central Pacific (CP), it was substantially weaker than the 1997/98 El Niño in the eastern Pacific (EP) ( L’Heureux et al. 2017 ). However, regarding ranking the strongest ENSO events while
al. 2016 ; Huang et al. 2016 ). The two other events occurred in 1982/83 and 1997/98. Sea surface temperature (SST) anomalies for the established ENSO indicator regions show that although the 2015/16 El Niño showed the largest anomalies over the Niño-3.4 region (in late 2015) that partly covers the central Pacific (CP), it was substantially weaker than the 1997/98 El Niño in the eastern Pacific (EP) ( L’Heureux et al. 2017 ). However, regarding ranking the strongest ENSO events while
