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,b ). Compound heat-wave events also typically persist for longer than either daytime or nighttime events ( Fig. 2c ). The statistics for the southern Great Plains are affected by the extreme summer of 2011, when 82 days in JJA were classified as heat-wave days, including one single event that lasted 44 days ( Fig. 2d ). Therefore, the gray bars in Fig. 2 show the statistics for compound heat waves over the southern Great Plains if 2011 is not included. The Northwest, northern Great Plains, and Southeast
,b ). Compound heat-wave events also typically persist for longer than either daytime or nighttime events ( Fig. 2c ). The statistics for the southern Great Plains are affected by the extreme summer of 2011, when 82 days in JJA were classified as heat-wave days, including one single event that lasted 44 days ( Fig. 2d ). Therefore, the gray bars in Fig. 2 show the statistics for compound heat waves over the southern Great Plains if 2011 is not included. The Northwest, northern Great Plains, and Southeast
depicted in Fig. 3 . Again, only the most notable results are included. Note that TOA terms are shown in Figs. 3a–d , while surface terms are in Figs. 3e–i . As expected, the TOA reflected SW flux ( Fig. 3a ) is largest during the Northern Hemisphere winter, when the sun shines on Antarctica and the clouds of the Southern Ocean, and smallest in the Northern Hemisphere late summer, when the sun is farthest from Earth and vegetation is at its greatest extent. (Lack of landmass in the midlatitudes
depicted in Fig. 3 . Again, only the most notable results are included. Note that TOA terms are shown in Figs. 3a–d , while surface terms are in Figs. 3e–i . As expected, the TOA reflected SW flux ( Fig. 3a ) is largest during the Northern Hemisphere winter, when the sun shines on Antarctica and the clouds of the Southern Ocean, and smallest in the Northern Hemisphere late summer, when the sun is farthest from Earth and vegetation is at its greatest extent. (Lack of landmass in the midlatitudes
eastern basin 3 over the central latitudes of the ice sheet indicate the center of the height anomaly is displaced northward and to the northwest of Iceland. The regression pattern for basin 7 is similar to that of basin 3 but with the height anomaly located slightly to the south and west. The regression map for northeastern basin 2 shows positive height anomalies over the basin and centered along the southern boundary, but a negative height anomaly over the central Arctic Ocean is also indicated. The
eastern basin 3 over the central latitudes of the ice sheet indicate the center of the height anomaly is displaced northward and to the northwest of Iceland. The regression pattern for basin 7 is similar to that of basin 3 but with the height anomaly located slightly to the south and west. The regression map for northeastern basin 2 shows positive height anomalies over the basin and centered along the southern boundary, but a negative height anomaly over the central Arctic Ocean is also indicated. The
reanalyses. The motivation for this investigation is to explain the MERRA-2 water cycle data, especially the effects of the water vapor analysis increments on the physical terms in the water cycle. To accomplish this we will characterize the global water cycle compared to a merged and balanced observational dataset and also evaluate the temporal variations of the budget terms ( section 4 ). From this we can determine the ocean–land transport of water in MERRA-2. The investigation will focus on the
reanalyses. The motivation for this investigation is to explain the MERRA-2 water cycle data, especially the effects of the water vapor analysis increments on the physical terms in the water cycle. To accomplish this we will characterize the global water cycle compared to a merged and balanced observational dataset and also evaluate the temporal variations of the budget terms ( section 4 ). From this we can determine the ocean–land transport of water in MERRA-2. The investigation will focus on the
incorporating the Niño-3.4 SST data uncertainty, Huang et al. (2016) pointed out that the strength of the three strongest events is not clearly separable at the 95% confidence level. The multivariate El Niño–Southern Oscillation index (MEI; Wolter and Timlin 1998 ) that combines analysis of multiple meteorological and oceanographic components indicates that 1997/98 is the strongest El Niño event during the El Niño developing stage (i.e., 1982, 1997, and 2015). Our analysis of the atmospheric and oceanic
incorporating the Niño-3.4 SST data uncertainty, Huang et al. (2016) pointed out that the strength of the three strongest events is not clearly separable at the 95% confidence level. The multivariate El Niño–Southern Oscillation index (MEI; Wolter and Timlin 1998 ) that combines analysis of multiple meteorological and oceanographic components indicates that 1997/98 is the strongest El Niño event during the El Niño developing stage (i.e., 1982, 1997, and 2015). Our analysis of the atmospheric and oceanic
abundant. There is no similar effect in the Northern Hemisphere, where surface pressure observations from land stations are dominant early in the period; the observation errors specified for these data are the same in MERRA and MERRA-2. Finally, the jump in RMS values in the Southern Hemisphere evident in both reanalyses at the beginning of 1985 coincides with the introduction of regularly spaced synthetic surface pressure observations over Southern Ocean areas. Fig . 6. Monthly mean (thick lines) and
abundant. There is no similar effect in the Northern Hemisphere, where surface pressure observations from land stations are dominant early in the period; the observation errors specified for these data are the same in MERRA and MERRA-2. Finally, the jump in RMS values in the Southern Hemisphere evident in both reanalyses at the beginning of 1985 coincides with the introduction of regularly spaced synthetic surface pressure observations over Southern Ocean areas. Fig . 6. Monthly mean (thick lines) and
global number of observations (1982–2014) from AVHRR NNR (blue), MODIS Terra over ocean (MODO NNR; light blue) and land (MODL NNR; green), MISR (magenta) over bright surfaces (deserts), MODIS Aqua over ocean (MYDO NNR; gray) and land (MYDL NNR; pink), and AERONET (yellow) where NNR is the bias-corrected neural net retrieved AOD. Note the following: 1) AVHRR observations are only over the ocean. 2) Stronger cloud contamination in the Southern Hemisphere relative to the Northern Hemisphere imparts
global number of observations (1982–2014) from AVHRR NNR (blue), MODIS Terra over ocean (MODO NNR; light blue) and land (MODL NNR; green), MISR (magenta) over bright surfaces (deserts), MODIS Aqua over ocean (MYDO NNR; gray) and land (MYDL NNR; pink), and AERONET (yellow) where NNR is the bias-corrected neural net retrieved AOD. Note the following: 1) AVHRR observations are only over the ocean. 2) Stronger cloud contamination in the Southern Hemisphere relative to the Northern Hemisphere imparts
is stronger and slightly farther south in the 95th percentile anomaly composite (cf. Figs. 5c,d ). Moisture flux anomalies are strongest in the southern portion of the circulation. Anomalies are likely stronger to the south from the convergence of moisture coming from the west and moisture from the Gulf of Mexico. The increased flow of moisture from the Atlantic Ocean apparent in the 95th percentile composite for the day prior to the event is somewhat deceptive. In that region, moisture fluxes
is stronger and slightly farther south in the 95th percentile anomaly composite (cf. Figs. 5c,d ). Moisture flux anomalies are strongest in the southern portion of the circulation. Anomalies are likely stronger to the south from the convergence of moisture coming from the west and moisture from the Gulf of Mexico. The increased flow of moisture from the Atlantic Ocean apparent in the 95th percentile composite for the day prior to the event is somewhat deceptive. In that region, moisture fluxes
Pacific (RSMC Honolulu) using 1-min averaging periods; the north Indian Ocean (RSMC New Delhi) using a 3-min period; and the other agencies using 10-min averaging periods ( Schreck et al. 2014 ). The 10-min wind speeds are converted to 1-min wind speeds using a factor of 1.13, which has traditionally been used ( Harper et al. 2010 ), and the data from RSMC Miami and New Delhi are used in their original form. However, there are uncertainties in the accuracy and fidelity of this conversion, with
Pacific (RSMC Honolulu) using 1-min averaging periods; the north Indian Ocean (RSMC New Delhi) using a 3-min period; and the other agencies using 10-min averaging periods ( Schreck et al. 2014 ). The 10-min wind speeds are converted to 1-min wind speeds using a factor of 1.13, which has traditionally been used ( Harper et al. 2010 ), and the data from RSMC Miami and New Delhi are used in their original form. However, there are uncertainties in the accuracy and fidelity of this conversion, with
and poor quality of the gauge observations ( section 2b ). In this region, the MERRA-2 land surface experiences the M2AGCM precipitation (i.e., w = 0 in Figs. 1 and 2 ). In the Northern and Southern Hemisphere latitude bands between 42.5° and 62.5°, the precipitation corrections are tapered linearly between full corrections based on CPCU (at 42.5°) and no corrections (at 62.5°). Over the oceans (not shown in Fig. 2 ), the M2CORR precipitation is based on (rescaled) CMAP observations and uses
and poor quality of the gauge observations ( section 2b ). In this region, the MERRA-2 land surface experiences the M2AGCM precipitation (i.e., w = 0 in Figs. 1 and 2 ). In the Northern and Southern Hemisphere latitude bands between 42.5° and 62.5°, the precipitation corrections are tapered linearly between full corrections based on CPCU (at 42.5°) and no corrections (at 62.5°). Over the oceans (not shown in Fig. 2 ), the M2CORR precipitation is based on (rescaled) CMAP observations and uses
