Sensor requirements for Earth and planetary observations

Future generations of Earth and planetary remote sensing instruments will require extensive developments of new long-wave and very long-wave infrared detectors. The upcoming NASA Earth Observing System (EOS) will carry a suite of instruments to monitor a wide range of atmospheric and surface parameters with an unprecedented degree of accuracy for a period of 10 to 15 years. These instruments will observe Earth over a wide spectral range extending from the visible to nearly 17 micrometers with a moderate to high spectral and spacial resolution. In addition to expected improvements in communication bandwidth and both ground and on-board computing power, these new sensor systems will need large two-dimensional detector arrays. Such arrays exist for visible wavelengths and, to a lesser extent, for short wavelength infrared systems. The most dramatic need is for new Long Wavelength Infrared (LWIR) and Very Long Wavelength Infrared (VLWIR) detector technologies that are compatible with area array readout devices and can operate in the temperature range supported by long life, low power refrigerators. A scientific need for radiometric and calibration accuracies approaching 1 percent translates into a requirement for detectors with excellent linearity, stability and insensitivity to operating conditions and space radiation. Current examples of the kind of scientific missions these new thermal IR detectors would enhance in the future include instruments for Earth science such as Orbital Volcanological Observations (OVO), Atmospheric Infrared Sounder (AIRS), Moderate Resolution Imaging Spectrometer (MODIS), and Spectroscopy in the Atmosphere using Far Infrared Emission (SAFIRE). Planetary exploration missions such as Cassini also provide examples of instrument concepts that could be enhanced by new IR detector technologies

The Mechanical Energies of the Global Atmosphere in El Niño and La Niña Years

Two meteorological reanalysis datasets are analyzed to determine the mechanical energies of the global atmosphere in the El Niño and La Niña years. The general consistency of the mean energy components between the two datasets reveals ~1%–3% increase and ~2%–3% decrease in the mean energies in the El Niño years and La Niña years, respectively. These analyses further reveal that the tropospheric temperature responds to the sea surface temperature anomaly with a time lag of two months, which leads to the varying mean atmospheric energies in the El Niño and La Niña years

The influence of tropospheric biennial oscillation on mid-tropospheric CO_2

Mid-tropospheric CO_2 retrieved from the Atmospheric Infrared Sounder (AIRS) was used to investigate CO_2 interannual variability over the Indo-Pacific region. A signal with periodicity around two years was found for the AIRS mid-tropospheric CO_2 for the first time, which is related to the Tropospheric Biennial Oscillation (TBO) associated with the strength of the monsoon. During a strong (weak) monsoon year, the Western Walker Circulation is strong (weak), resulting in enhanced (diminished) CO_2 transport from the surface to the mid-troposphere. As a result, there are positive (negative) CO2 anomalies at mid-troposphere over the Indo-Pacific region. We simulated the influence of the TBO on the mid-tropospheric CO_2 over the Indo-Pacific region using the MOZART-2 model, and results were consistent with observations, although we found the TBO signal in the model CO_2 is to be smaller than that in the AIRS observations

The recycling rate of atmospheric moisture over the past two decades (1988–2009)

Numerical models predict that the recycling rate of atmospheric moisture decreases with time at the global scale, in response to global warming. A recent observational study (Wentz et al 2007 Science 317 233–5) did not agree with the results from numerical models. Here, we examine the recycling rate by using the latest data sets for precipitation and water vapor, and suggest a consistent view of the global recycling rate of atmospheric moisture between numerical models and observations. Our analyses show that the recycling rate of atmospheric moisture has also decreased over the global oceans during the past two decades. In addition, we find different temporal variations of the recycling rate in different regions when exploring the spatial pattern of the recycling rate. In particular, the recycling rate has increased in the high-precipitation region around the equator (i.e., the intertropical convergence zone) and decreased in the low-precipitation region located either side of the equator over the past two decades. Further exploration suggests that the temporal variation of precipitation is stronger than that of water vapor, which results in the positive trend of the recycling rate in the high-precipitation region and the negative trend of the recycling rate in the low-precipitation region

CO_2 semiannual oscillation in the middle troposphere and at the surface

Using in situ measurements, we find a semiannual oscillation (SAO) in the midtropospheric and surface CO_2. Chemistry transport models (2-D Caltech/JPL model, 3-D GEOS-Chem, and 3-D MOZART-2) are used to investigate possible sources for the SAO signal in the midtropospheric and surface CO_2. From model sensitivity studies, it is revealed that the SAO signal in the midtropospheric CO_2 originates mainly from surface CO_2 with a small contribution from transport fields. It is also found that the source for the SAO signal in surface CO_2 is mostly related to the CO_2 exchange between the biosphere and the atmosphere. By comparing model CO_2 with in situ CO_2 measurements at the surface, we find that models are able to capture both annual and semiannual cycles well at the surface. Model simulations of the annual and semiannual cycles of CO_2 in the tropical middle troposphere agree reasonably well with aircraft measurements

Simulation of upper tropospheric CO₂ from chemistry and transport models

The California Institute of Technology/Jet Propulsion Laboratory two-dimensional (2-D), three-dimensional (3-D) GEOS-Chem, and 3-D MOZART-2 chemistry and transport models (CTMs), driven respectively by NCEP2, GEOS-4, and NCEP1 reanalysis data, have been used to simulate upper tropospheric CO2 from 2000 to 2004. Model results of CO2 mixing ratios agree well with monthly mean aircraft observations at altitudes between 8 and 13 km (Matsueda et al., 2002) in the tropics. The upper tropospheric CO2 seasonal cycle phases are well captured by the CTMs. Model results have smaller seasonal cycle amplitudes in the Southern Hemisphere compared with those in the Northern Hemisphere, which are consistent with the aircraft data. Some discrepancies are evident between the model and aircraft data in the midlatitudes, where models tend to underestimate the amplitude of CO2 seasonal cycle. Comparison of the simulated vertical profiles of CO2 between the different models reveals that the convection in the 3-D models is likely too weak in boreal winter and spring. Model sensitivity studies suggest that convection mass flux is important for the correct simulation of upper tropospheric CO2

Interannual variability of mid-tropospheric CO_2 from Atmospheric Infrared Sounder

Atmospheric Infrared Sounder (AIRS) offers a unique opportunity to investigate the variability of mid-tropospheric CO_2 over the entire globe. In this paper, we use AIRS data to examine the interannual variability of CO_2 and find significant correlations between AIRS mid-tropospheric CO_2 and large-scale atmospheric dynamics. During El Niño events, mid-tropospheric CO_2 over the central Pacific Ocean is enhanced whereas it is reduced over the western Pacific Ocean as a result of the change in the Walker circulation. The variation of AIRS CO_2 in the high latitudes of the northern hemisphere is closely related to the strength of the northern hemispheric annular mode. These results contribute to a better understanding of the influence of large-scale dynamics on tracer distributions

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation