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Our complex climate system has trapped 400 ZettaJ (10 21 ) and has many positive feedbacks. Regions are changing at unprecedented rates and crossing thresholds as BAU will lock us to 8.5 W m -2 in 2100.
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Results are presented for six simulations of the Goddard Institute for Space Studies (GISS) global atmosphere-ocean model for the years 1950 to 2099. There are two control simulations with constant 1950 atmospheric composition from different initial states, two GHG experiments with observed greenhouse gases up to 1990 and compounded .5% CO2 annual increases thereafter, and two GHG+SO4 experiments with the same varying greenhouse gases plus varying tropospheric sulfate aerosols. Surface air temperature trends in the two GHG experiments are compared between themselves and with the observed temperature record from 1960 and 1998. All comparisons show high positive spatial correlation in the northern hemisphere except in summer when the greenhouse signal is weakest. The GHG+SO4 experiments show weaker correlations. In the southern hemisphere, correlations are either weak or negative which in part are due to the model's unrealistic interannual variability of southern sea ice cover. The model results imply that temperature changes due to forcing by increased greenhouse gases have risen above the level of regional interannual temperature variability in the northern hemisphere over the past 40 years. This period is thus an important test of reliability of coupled climate models.
A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.
We employed an atmospheric transport model to attribute column-averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1deg × 0.1deg). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 +/- 0.38 (p<0.1), but with a larger ratio over East Asia (1.22 +/- 0.32; p<0.05). The obtained observation-model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (approx.15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.
The Alpha Jet Atmospheric eXperiment (AJAX), located in the Bay Area of California, is a joint effort between NASA Ames Research Center and H211, LCC. AJAX makes in-situ airborne measurements of trace gases 2-4 times per month, resulting in over 216 flights since 2011. Current measurements include ozone (O3), carbon dioxide (CO2), methane (CH4), water (H2O), formaldehyde (HCHO), and meteorological measurements (i.e., ambient pressure, temperature, and 3D winds). Currently, the AJAX team is working to incorporate nitrogen dioxide (NO2) measurements with a Cavity Attenuated Phase Shift Spectrometer (CAPS). Successful science flights coincident with satellite overpasses have been performed since 2011 by the Alpha Jet, with more than 40 flights under the Greenhouse Observing SATellite (GOSAT) and several flights under the Orbiting Carbon Observatory-2 (OCO-2). Results from these flights, which have covered a range of different surfaces and seasonal conditions, will be presented. In-situ vertical profiles of O3, CO2, CH4, H2O, HCHO, and NO2 from the surface to 28,000 feet made by AJAX will also be valuable for satellite validation of data products obtained from the TROPOspheric Montoring Instrument (TROPOMI). TROPOMI is on board the Copernicus Sentinel-5 precursor (S5p) satellite, with level 2 products including O3, CO, CH4, HCHO, NO2, and aerosols.
The Greenhouse Gas (GhG) Measurement system is a combination of two systems in series: (1) the Tower Gas Processing (TGP) System, an instrument rack which pulls, pressurizes, and dries air streams from an atmospheric sampling tower through a series of control and monitoring components, and (2) the Picarro model G2301 cavity ringdown spectrometer (CRDS), which measures CO2, CH4, and H2O vapor; the primary measurements of the GhG system.
We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.
Greenhouse trapping is examined theoretically using a version of the radiative transfer equations that demonstrates how atmospheric greenhouse trapping can vary. Satellite observations of atmospheric greenhouse trapping are examined for four months representing the various seasons. The cause of the super greenhouse effect at the highest SSTs is examined, and four processes are found to contribute. The middle and upper troposphere must be particularly moist and the temperature lapse rate must be increasingly unstable over the warmest regions to explain the observed distribution of atmospheric greenhouse trapping. Since the highest SSTs are generally associated with deep convection, this suggests that deep convection acts to moisten the middle and upper troposphere in regions of the highest SSTs relative to other regions. The tropical atmospheric circulation acts to both increase the temperature lapse rate and greatly increase the atmospheric water vapor concentration with spatially increasing SST.
Observational evidence reveals a pronounced wetting trend over Central Asia in recent decades, with the most substantial increases occurring during winter and summer. Yet the extent to which the drivers of these changes differ seasonally remains unknown. Here, we use single-forcing experiments from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to examine the effects of various external forcings on winter and summer precipitation across Central Asia and to explore the physical mechanisms underlying seasonal precipitation changes. We find that greenhouse gas (GHG) forcing mainly increases winter precipitation by enhancing atmospheric moisture content through warming. In contrast, in summer, Asian sulfate aerosols enhance precipitation by modulating the westerly jet, which strengthens atmospheric moisture transport into the region. Asian black carbon exerts an opposing influence that partially offsets the sulfate-induced effect. Further attribution analysis based on CMIP6 simulations reinforces these sensitivity results and shows that GHG forcing is the primary driver of winter precipitation increases whereas anthropogenic aerosols dominate summer trends. Future CMIP6 projections suggest that under moderate- to high-emission scenarios, winter precipitation will continue to rise due to increasing GHG concentrations, while summer precipitation may decline across much of Central Asia as a result of reduced aerosol emissions following Asian clean air policies. These findings highlight a distinct seasonality in the drivers of recent precipitation increase and suggest a plausible divergence in future winter and summer precipitation trends.
Over the 20th and 21st centuries, both anthropogenic greenhouse gas increases and changes in anthropogenic aerosols have affected rainfall in the Sahel. Using multiple characteristics of Sahel precipitation, we construct a multivariate fingerprint that allows us to distinguish between the model-predicted responses to greenhouse gases and anthropogenic aerosols. Models project the emergence of a detectable signal of aerosol forcing in the middle of the 20th century and a detectable signal of greenhouse gas forcing at the beginning of the 21st. However, the signals of both aerosol and greenhouse gas forcing in observations emerge earlier and are stronger than in the models, far stronger in the case of aerosols. The similarity between the response to aerosol forcing and the leading mode of internal variability makes it difficult to attribute this model-observation discrepancy to errors in the forcing, errors in the forced response, model inability to capture the amplitude of internal variability, or some combination of these. For greenhouse gases, however, the forced response is distinct from internal variability as estimated by models, and the observations are largely commensurate with the model projections.
Over the 20th and 21st centuries, both anthropogenic greenhouse gas increases and changes in anthropogenic aerosols have affected rainfall in the Sahel. Using multiple characteristics of Sahel precipitation, we construct a multivariate fingerprint that allows us to distinguish between the model-predicted responses to greenhouse gases and anthropogenic aerosols. Models project the emergence of a detectable signal of aerosol forcing in the middle of the 20th century and a detectable signal of greenhouse gas forcing at the beginning of the 21st. However, the signals of both aerosol and greenhouse gas forcing in observations emerge earlier and are stronger than in the models, far stronger in the case of aerosols. The similarity between the response to aerosol forcing and the leading mode of internal variability makes it difficult to attribute this model-observation discrepancy to errors in the forcing, errors in the forced response, model inability to capture the amplitude of internal variability, or some combination of these. For greenhouse gases, however, the forced response is distinct from internal variability as estimated by models, and the observations are largely commensurate with the model projections.