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primary focus here. Section 2 describes the data and analysis methods. Section 3 details the results, starting with an assessment of the climatology and trends in heat-wave frequency over the United States and followed by a composite analysis to investigate mechanisms that contribute to heat waves. A summary and conclusions are provided in section 4 . 2. Data and methods a. MERRA-2 The National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and
primary focus here. Section 2 describes the data and analysis methods. Section 3 details the results, starting with an assessment of the climatology and trends in heat-wave frequency over the United States and followed by a composite analysis to investigate mechanisms that contribute to heat waves. A summary and conclusions are provided in section 4 . 2. Data and methods a. MERRA-2 The National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and
challenge. Second, despite the best efforts at harmonizing the observing system through quality control, differences in data coverage can and do impact the analyzed AOD, particularly between the pre- and post-NASA Earth Observing System (EOS) periods (1980–99 and 2000 onward, respectively). Finally, nonanalyzed aerosol properties (e.g., vertical distribution, aerosol speciation, absorption) are not fully constrained by the assimilation and strongly resemble the assimilating model in most cases
challenge. Second, despite the best efforts at harmonizing the observing system through quality control, differences in data coverage can and do impact the analyzed AOD, particularly between the pre- and post-NASA Earth Observing System (EOS) periods (1980–99 and 2000 onward, respectively). Finally, nonanalyzed aerosol properties (e.g., vertical distribution, aerosol speciation, absorption) are not fully constrained by the assimilation and strongly resemble the assimilating model in most cases
quality of these fields has not encouraged the atmospheric ozone community to use them in scientific research. Typically, researchers prefer to utilize satellite and in situ ozone data along with assimilated meteorological variables. To our knowledge, the only comprehensively validated reanalysis ozone fields are those from two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses: ERA-40 ( Dethof and Hólm 2004 ) and ERA-Interim ( Dragani 2011 ). On the other hand, a large body of
quality of these fields has not encouraged the atmospheric ozone community to use them in scientific research. Typically, researchers prefer to utilize satellite and in situ ozone data along with assimilated meteorological variables. To our knowledge, the only comprehensively validated reanalysis ozone fields are those from two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses: ERA-40 ( Dethof and Hólm 2004 ) and ERA-Interim ( Dragani 2011 ). On the other hand, a large body of
tends to bring MERRA-2 profiles closer to the observations than the control simulation (M2REPLAY) profiles. c. Surface mass—PM 2.5 over the United States Fine aerosols near the surface with diameters less than 2.5 μ m, known as PM 2.5 , negatively impact both air quality and human health (e.g., Pope et al. 2009 ; Pope and Dockery 2013 ). Air quality monitoring networks exist in various regions over the globe, but they offer sparse geographical and temporal coverage. The use of data assimilation
tends to bring MERRA-2 profiles closer to the observations than the control simulation (M2REPLAY) profiles. c. Surface mass—PM 2.5 over the United States Fine aerosols near the surface with diameters less than 2.5 μ m, known as PM 2.5 , negatively impact both air quality and human health (e.g., Pope et al. 2009 ; Pope and Dockery 2013 ). Air quality monitoring networks exist in various regions over the globe, but they offer sparse geographical and temporal coverage. The use of data assimilation
Hemisphere ( Fig. 6b ), the RMS values are larger in MERRA-2 than in MERRA before the mid-1990s but smaller by the end of the period. The larger values early on are due to the use of larger observation errors for surface ship observations (and some other conventional data types) in MERRA-2, allowing more “outliers” with larger departure values to pass the quality control procedure in the analysis. 1 The impact diminishes by the mid-1990s as other observation types, including from satellites, become more
Hemisphere ( Fig. 6b ), the RMS values are larger in MERRA-2 than in MERRA before the mid-1990s but smaller by the end of the period. The larger values early on are due to the use of larger observation errors for surface ship observations (and some other conventional data types) in MERRA-2, allowing more “outliers” with larger departure values to pass the quality control procedure in the analysis. 1 The impact diminishes by the mid-1990s as other observation types, including from satellites, become more
(temporally and spatially) coarser precipitation data product that is based on satellite as well as gauge observations. The latter two changes were made because the sparse coverage and poor quality of the gauge-only precipitation product in Africa and at high latitudes had a detrimental impact on the MERRA-Land product ( Mudryk et al. 2015 , their Fig. 2; Reichle et al. 2017 ). Reanalysis products are used extensively for land surface research and applications. In particular, many studies rely on near
(temporally and spatially) coarser precipitation data product that is based on satellite as well as gauge observations. The latter two changes were made because the sparse coverage and poor quality of the gauge-only precipitation product in Africa and at high latitudes had a detrimental impact on the MERRA-Land product ( Mudryk et al. 2015 , their Fig. 2; Reichle et al. 2017 ). Reanalysis products are used extensively for land surface research and applications. In particular, many studies rely on near
for the computation of the other metrics). Unless noted otherwise, the processing and extensive quality control of the in situ measurements and the computation of the metrics match that of De Lannoy and Reichle (2016) ; their Fig. 5 illustrates the locations of the sensors. After quality control of the hourly data, the in situ measurements were aggregated into daily averages, metrics were computed from the daily data for each site separately, and the results were then averaged using a spatial
for the computation of the other metrics). Unless noted otherwise, the processing and extensive quality control of the in situ measurements and the computation of the metrics match that of De Lannoy and Reichle (2016) ; their Fig. 5 illustrates the locations of the sensors. After quality control of the hourly data, the in situ measurements were aggregated into daily averages, metrics were computed from the daily data for each site separately, and the results were then averaged using a spatial
difference between the reanalyses is that the NCEP-CFSR uses a technique to improve the representation of TCs by adjusting the location of the tropical vortex to its observed location before the assimilation of storm circulation observations ( Saha et al. 2010 ). The MERRA-2 reanalysis also uses this method. All the reanalyses in this study make use of quality control processes and bias correction for the diverse range of observations that are assimilated, such as the variational bias correction of
difference between the reanalyses is that the NCEP-CFSR uses a technique to improve the representation of TCs by adjusting the location of the tropical vortex to its observed location before the assimilation of storm circulation observations ( Saha et al. 2010 ). The MERRA-2 reanalysis also uses this method. All the reanalyses in this study make use of quality control processes and bias correction for the diverse range of observations that are assimilated, such as the variational bias correction of
mean, minimum, and maximum temperature over land ( Harris et al. 2014a , b ). The temperatures are calculated from quality-controlled climate station data, which are interpolated onto the grid according to an assumed correlation decay distance (set to 1200 km for temperature variables). In instances where no station data are available within the assumed decay distance, the published data value defaults to the climatology. Here, such climatological values have been screened out. Also, we require at
mean, minimum, and maximum temperature over land ( Harris et al. 2014a , b ). The temperatures are calculated from quality-controlled climate station data, which are interpolated onto the grid according to an assumed correlation decay distance (set to 1200 km for temperature variables). In instances where no station data are available within the assumed decay distance, the published data value defaults to the climatology. Here, such climatological values have been screened out. Also, we require at
from model physics formulation but also from the quality of the forcing data ( Badgley et al. 2015 ). Precipitation datasets [e.g., GPCC, Global Precipitation Climatology Project (GPCP), and others] differ in sampling, gauge undercatch, and data quality. More problematic is near-surface meteorology and radiative forcing. These variables are taken from reanalyses also but are bias adjusted using surface observations and satellite radiative fluxes (e.g., Sheffield et al. 2006 ; Weedon et al. 2011
from model physics formulation but also from the quality of the forcing data ( Badgley et al. 2015 ). Precipitation datasets [e.g., GPCC, Global Precipitation Climatology Project (GPCP), and others] differ in sampling, gauge undercatch, and data quality. More problematic is near-surface meteorology and radiative forcing. These variables are taken from reanalyses also but are bias adjusted using surface observations and satellite radiative fluxes (e.g., Sheffield et al. 2006 ; Weedon et al. 2011
