The B-29 was the progenitor of a series of Boeing-built bombers, transports, tankers, reconnaissance aircraft, and trainers. For example, the re-engined B-50 Superfortress Lucky Lady II became the first aircraft to fly around the world non-stop, during a 94-hour flight in 1949. The Boeing C-97 Stratofreighter airlifter, which was first flown in 1944, was followed in 1947 by its commercial airliner variant, the Boeing Model 377 Stratocruiser. This bomber-to-airliner derivation was similar to the B-17/Model 307 evolution. In 1948, Boeing introduced the KB-29 tanker, followed in 1950 by the Model 377-derivative KC-97. A line of outsized-cargo variants of the Stratocruiser is the Guppy / Mini Guppy / Super Guppy, which remain in service with NASA and other operators. The Soviet Union produced 847 Tupolev Tu-4s, an unlicensed reverse-engineered copy of the B-29. Twenty B-29s remain as static displays, but only two, FIFI and Doc, still fly.[6]
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This pessimistic assessment of pathology is corroborated by clinical experience. Although not universal (Gudeman et al., 1979), a large number of studies suggest that the development of HPC is associated with a worse clinical course and higher rates of mortality (Allard et al., 2009; Chieregato et al., 2005; Nelson et al., 2010; Oertel et al., 2002; Servadei et al., 2000b; Stein et al., 1992,1993; Tian et al., 2010). In the study by Stein and associates (Stein et al., 1993), delayed injury was associated with higher mortality, slowed recovery, and poorer outcome at 6 months; the authors concluded that HPC is associated with dramatically worse outcomes for each category of initial injury severity. Servadei and colleagues (Servadei et al., 2000a) found that when the initial CT scan demonstrated a diffuse injury without swelling or shift, evolution to a mass lesion was associated with a statistically significant increase in the risk of an unfavorable outcome (62% versus 38%). In the study by Chieregato and co-workers (Chieregato et al., 2005), patients who experienced significant progression of their lesion had a significantly higher risk of an unfavorable outcome (32% versus 10%). Allard and associates (Allard et al., 2009) found that progression was associated with a fivefold higher risk of death (32% versus 8.6%).
Currently, our knowledge on the animal origin of SARS-CoV-2 remains incomplete to a large part. The reservoir hosts of the virus have not been clearly proven. It is unknown whether SARS-CoV-2 was transmitted to humans through an intermediate host and which animals may act as its intermediate host. Detection of RaTG13, RmYN02 and pangolin coronaviruses implies that diverse coronaviruses similar to SARS-CoV-2 are circulating in wildlife. In addition, as previous studies showed recombination as the potential origin of some sarbecoviruses such as SARS-CoV, it cannot be excluded that viral RNA recombination among different related coronaviruses was involved in the evolution of SARS-CoV-2. Extensive surveillance of SARS-CoV-2-related viruses in China, Southeast Asia and other regions targeting bats, wild and captured pangolins and other wildlife species will help us to better understand the zoonotic origin of SARS-CoV-2.
A key component in the seasonal and interannual evolution of Arctic sea ice is the timing of melt onset and freeze up. Surface melt and freeze up can be obtained from satellite passive microwave observations but satellite observations do not provide information about what is happening under the ice. The length of the melt season is much longer underneath sea ice as a result of ocean heat. A recent study evaluated data collected from sea ice mass balance buoys (IMBs) and upward looking sonar (ULS) instruments to gain insights on the seasonality of ice melt and freeze up below the ice. Over the period from 2001 to 2018, Lin and colleagues found that in the Beaufort Sea, the bottom melt onset began 17 days before the surface melt. By contrast, in the central Arctic the timing of the melt onset of the top and bottom of the ice was similar. The freeze up of the bottom of the ice began about three weeks after the surface started to freeze. The researchers also found that bottom melt onset occurred about a week earlier during the 2010 to 2018 period compared to the 2001 to 2009 period, because of rising ocean temperatures. Bottom freeze up began one to two weeks earlier in the later period compared to the earlier period because thinner ice allowed faster heat loss from the ocean.
The animation above shows the evolution of ozone over the South Pole between January 1 and October 7, 2021. Notice that moderate ozone losses (orange) are apparent by late August and become even more potent (red) and widespread through September. The ozone hole reached its maximum extent on October 7, 2021, as calculated by the NASA Ozone Watch team.
As a consequence of climate change, the global and regional mean sea level will change. Coupled climate models are used to make projections of the climate changes and the associated SLR. Results from the CMIP5 model archive used for AR5 provide information on expected changes in the oceans and on the evolution of climate, glaciers and ice sheets. New estimates from CMIP6 are not yet available and will be discussed in the IPCC 6th Assessment Report (AR6), hence only a partly updated projection can be presented here.
Coupled climate models can be applied on century time scales, to provide estimates of the steric (temperature and salinity effects on sea water density) and ocean dynamical (ocean circulation) components of sea level change, both globally and regionally. However, the glacier and ice sheet component are calculated off-line based on temperature and precipitation changes. In the AR5 report, changes in the SMB of glaciers and ice sheet were calculated from the global surface air temperature. In addition, GCMs also resolve climate variability related to changes in precipitation and evaporation. These changes are used to calculate short duration sea level changes (Cazenave and Cozannet, 2014363; Hamlington et al., 2017364). With various degrees of success those models capture ENSO, PDO and other modes of variability (e.g., Yin et al., 2009; Zhang and Church, 2012365), which affect sea level through redistributions of energy and salt in the ocean on slightly longer time scales. Off-line temperature and precipitation fields can be dynamically or statistically downscaled to match the high spatial resolution required for ice sheets and glaciers, but serious limitations remain. This deficiency limits adequate representation of potentially important feedbacks between changes in ice sheet geometry and climate, for example through fresh water and iceberg production that impact on ocean circulation and sea ice, which can have global consequences (Lenaerts et al., 2016366; Donat-Magnin et al., 2017367). Another limitation is the lack of coupling with the solid Earth which controls the ice sheet evolution (Whitehouse et al., 2019368). Dynamics of the interaction of ice streams with bedrock and till at the ice base remain difficult to model due to lack of direct observations. Nevertheless, several new ice sheet models have been generated over the last few years, particularly for Antarctica (Section 4.2.3.1) focusing on the dynamic contribution of the ice sheet to sea level change, which remains the key uncertainty in future projections (Church et al., 2013), particularly beyond 2050 (Kopp et al., 2014370; Nauels et al., 2017b371; Slangen et al., 2017a372; Horton et al., 2018373).
The GIS is currently losing mass at roughly twice the pace of the AIS (see Chapter 3 and Table 4.1). About 60% of the mass loss between 1991 and 2015 has been attributed to increasingly negative SMB from surface melt and runoff on the lower elevations of the ice sheet margin. Ice dynamical changes and increased discharge of marine-terminating glaciers account for the remaining 40% of mass loss (Csatho et al., 2014376; Enderlin, 2014377; van den Broeke et al., 2016378). The ability of firn on Greenland to retain meltwater until it refreezes has diminished markedly since the late 1990s, especially in lower elevations and on peripheral ice caps (Noël et al., 2017379). Patterns of surface melt on Greenland are highly dependent on regional atmospheric patterns (Bevis et al., 2019380), adding uncertainty to future projections of SMB. Melt-albedo feedbacks associated with darkening of the ice surface from ponded water, changes in snow and firn properties, and accumulation of impurities are also important, because they can strongly enhance surface melt (Tedesco et al., 2016381; Ryan et al., 2018382; Trusel et al., 2018383; Ryan et al., 2019384). These processes are not fully captured by most Greenland-scale models which is an important deficiency, because surface processes tend to dominate uncertainty in future GIS model projections (e.g., Edwards et al., 2014; Aschwanden et al., 2019385). Increases in meltwater and changes in the basal hydrologic regime, once thought to have a possible destabilising effect on the ice sheet (Zwally et al., 2002386), have been linked with recent reductions in ice velocity in western Greenland. On decadal time scales the effect of meltwater on ice dynamics are now assessed to be small (van de Wal et al., 2015387; Flowers, 2018388), which is supported by ice sheet model experiments (Shannon et al., 2013389). In sum, uncertain climate projections (Edwards et al., 2014390), albedo evolution, uncertainties around meltwater buffering by firn, complex processes linking surface, englacial and basal hydrology with ice dynamics (Goelzer et al., 2013391; Stevens et al., 2016392; Noël et al., 2017393; Hempelmann et al., 2018394) and meltwater induced melting at marine-terminating ice fronts (Chauché et al., 2014395), and coarse spatial model resolution (Pattyn et al., 2018396), all continue to provide substantial challenges for ice sheet and SMB models. 2ff7e9595c
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