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The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies. [ABSTRACT FROM AUTHOR]

This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors. A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies. Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical. [ABSTRACT FROM AUTHOR]

The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap. [ABSTRACT FROM AUTHOR]

Radiative transfer models for scattering atmospheres that are accurate yet computationally efficient are required for many applications, such as data assimilation in numerical weather prediction. The successive-order-of-interaction (SOI) model is shown to satisfy these demands under a wide range of conditions. In particular, the model has an accuracy typically much better than 1 K for most microwave and submillimeter cases in precipitating atmospheres. Its speed is found to be comparable to or faster than the commonly used though less accurate Eddington model. An adjoint has been written for the model, and so Jacobian sensitivities can be quickly calculated. In addition to a conventional error assessment, the correlation between errors in different microwave channels is also characterized. These factors combine to make the SOI model an appealing candidate for many demanding applications, including data assimilation and optimal estimation, from microwave to thermal infrared wavelengths. [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb), to be launched in 2023, will be capable of measuring solar-induced chlorophyll fluorescence (SIF) and add to the current space-based record which started in 1995. GeoCarb will be unique as it will be the first geostationary satellite capable of sensing SIF over the American continents. Consequently, SIF measurements from GeoCarb can be performed much more flexibly compared to polar-orbiting platforms. With its scan mirror assembly, the instrument can point to any location on the Earth disc. This will allow measurements to be collected at various times of day, and measurement locations can be re-visited several times within a day. In expectation of the launch, we conduct an extensive, SIF-focused simulation study and explore the capability and limitations of the instrument and its particular sampling approach. Using cloud information from real measurements, as well as other observation- and model-based data, we produce over four million atmospheric simulations of GeoCarb measurements that the instrument would see throughout a full day. We then apply dedicated SIF retrieval algorithms on the simulated spectra and investigate the results along with cloud-screening performance and emergent retrieval biases and subsequent bias correction. Finally, we make comparisons with currently operating instruments where appropriate and show future science users of GeoCarb SIF what a typical day of measurements will yield. • Solar-induced chlorophyll fluorescence (SIF) from GeoCarb • Simulation study using millions of realistic scenes with clouds and aerosols • Investigation of GeoCarb SIF and comparison with existing instruments [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter Orbiting Carbon Observatory 2 (OCO-2) B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (∼ 50 %), improves the precision by 0.18 ppm (∼ 12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest-latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision. [ABSTRACT FROM AUTHOR]

The article focuses on regional impacts of COVID-19 on carbon dioxide detected worldwide from space. Topics include the activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions, and the atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019–2020 Indian Ocean Dipole.

Satellite-based observations of carbon dioxide (CO2) are sensitive to all processes that affect the propagation of radiation in the atmosphere, including scattering and absorption by atmospheric aerosols. Therefore, accurate retrievals of column-averaged CO2 (XCO2) benefit from detailed information on the aerosol conditions. This is particularly relevant for future missions focusing on observing anthropogenic CO2 emissions, such as the Copernicus Anthropogenic CO2 Monitoring mission (CO2M). To fully prepare for CO2M observations, it is informative to investigate existing observations in addition to other approaches. Our focus here is on observations from the NASA Orbiting Carbon Observatory -2 (OCO-2) mission. In the operational full-physics XCO2 retrieval used to generate OCO-2 level 2 products, the aerosol properties are known to have high uncertainty but their main objective is to facilitate CO2 retrievals. We evaluate the OCO-2 product from the point of view of aerosols by comparing the OCO-2 retrieved aerosol properties to collocated Moderate Resolution Imaging Spectro-radiometer (MODIS) Aqua Dark Target aerosol products. We find that there is a systematic difference between the aerosol optical depth (AOD, τ) values retrieved by the two instruments, such that τOCO−2 ∼ 0.4τMODIS. We also find a dependence of the XCO2 on the AOD difference, indicating an aerosol-induced effect in the XCO2 retrieval. In addition, we find a weak but statistically significant correlation between MODIS AOD and XCO2, which can be partly explained by natural covariance and co-emission of aerosols and CO2 but is partly masked by the aerosol-induced XCO2 bias. Furthermore, we find that issues in the OCO-2 aerosol retrieval may lead to misclassification of the quality flag for a small fraction of OCO-2 retrievals. Based on MODIS data, 4.1 % of low AOD cases are incorrectly classified as high AOD (low quality) pixels, while 16.5 % of high AOD cases are erroneously classified as low AOD (high quality) pixels. Finally, we investigate the effect of an AOD threshold on the fraction of acceptable XCO2 data. We find that relaxing the MODIS AOD threshold from 0.2 to 0.5 (at 550 nm), which is the goal for the CO2M, increases the fraction of acceptable data by 14 percentage points globally, and by 31 percentage points for urban areas. [ABSTRACT FROM AUTHOR]

Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest. [ABSTRACT FROM AUTHOR]

The presence of 3D cloud radiative effects in OCO-2 retrievals is demonstrated from an analysis of 2014–2019 OCO-2 XCO2 raw retrievals, bias-corrected XCO2bc data, ground-based Total Carbon Column Observation Network (TCCON) XCO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud and radiance fields. In approximate terms, 40 % (quality flag – QF = 0, land or ocean) and 73 % (QF = 1, land or ocean) of the observations are within 4 km of clouds. 3D radiative transfer calculations indicate that 3D cloud radiative perturbations at this cloud distance, for an isolated low-altitude cloud, are larger in absolute value than those due to a 1 ppm increase in CO 2. OCO-2 measurements are therefore susceptible to 3D cloud effects. Four 3D cloud metrics, based upon MODIS radiance and cloud fields as well as stand-alone OCO-2 measurements, relate XCO2bc–TCCON averages to 3D cloud effects. This analysis indicates that the operational bias correction has a nonzero residual 3D cloud bias for both QF = 0 and QF = 1 data. XCO2bc–TCCON averages at small cloud distances differ from those at large cloud distances by - 0.4 and - 2.2 ppm for the QF = 0 and QF = 1 data over the ocean. Mitigation of 3D cloud biases with a table lookup technique, which utilizes the nearest cloud distance (Distkm) and spatial radiance heterogeneity (CSNoiseRatio) 3D metrics, reduces QF = 1 ocean and land XCO2bc–TCCON averages from - 1 ppm to near ± 0.2 ppm. The ocean QF = 1 XCO2bc–TCCON averages can be reduced to the 0.5 ppm level if 60 % (70 %) of the QF = 1 data points are utilized by applying Distkm (CSNoiseRatio) metrics in a data screening process. Over land the QF = 1 XCO2bc–TCCON averages are reduced to the 0.5 (0.8) ppm level if 65 % (63 %) of the data points are utilized by applying Diastkm (CSNoiseRatio) data screening. The addition of more terms to the linear regression equations used in the current bias correction processing without data screening, however, did not introduce an appreciable improvement in the standard deviations of the XCO2bc–TCCON statistics. [ABSTRACT FROM AUTHOR]

Urban areas, where more than 55% of the global population gathers, contribute more than 70% of anthropogenic fossil fuel carbon dioxide (CO 2ff) emissions. Accurate quantification of CO 2ff emissions from urban areas is of great importance for formulating global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among "top-down" approaches, potentially allowing us to track CO 2ff emission changes over cities globally. However, their accuracy is still limited by incomplete background information, cloud blockages, aerosol contamination, and uncertainties in models and priori emission inventories. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO 2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO 2 (X CO2) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings over the global 70 most populated cities from 2014 to 2019 are marked as high-quality. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and the flux cross-sectional integration method). The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks with reduced uncertainties in emissions are better agreed across the three methods compared to the all-data tracks. This demonstrates that OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales. All three methods consistently suggested that the ratio medians are greater than 1, implying that the ODIAC slightly underestimated CO 2ff emissions over Lahore. Additionally, our estimation of the a posteriori CO 2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO 2 emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X CO2 retrievals. • 17% of OCO-2 soundings over the 70 most populated cities are of high-quality. • OCO-2 quality flags are useful filters of low-quality retrievals at city scale. • Posteriori fossil fuel CO 2 emissions over Lahore showed a 6.7% annual increase. • More high-quality tracks are needed to better constrain urban CO 2 emission trends. [ABSTRACT FROM AUTHOR]

We present CO 2 emission estimates for twenty power plants and related facilities in the United States, India, South Africa, Poland, Russia and South Korea, derived from space-based CO 2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Improvements to OCO-2 data quality and to our methodology yield improved results relative to earlier work. These new results include emission quantification for both larger and smaller power plants, the first power plant emission estimate based on ocean glint data and emissions from a small city with multiple industrial facilities. CO 2 emission estimates are compared against reported facility emissions where available, including high temporal resolution data for the eight US sites. The difference with respect to reported values for the US sites ranges from 1.4% to 26.7%, with a mean of 15.1%, although the estimated emission sum for all US sites is within 0.8% of the reported value, suggesting the errors are largely random. This finding reinforces the importance of revisit rate for future space-based emission monitoring systems and furthermore confirms that making multiple overpasses of a power plant can reduce errors to an accuracy useful to support climate policy. • We quantify CO 2 emissions from power plants using observations from NASA's OCO-2. • Sum of estimates for 8 US power plants is within 0.8% of sum of reported values. • Results reinforce importance of revisit rate for satellite CO 2 emission monitoring. • Multiple revisits per power plant can lead to CO 2 emission accuracy useful for climate policy. [ABSTRACT FROM AUTHOR]

• Absorption coefficients for the Orbiting Carbon Observatory missions updated to v5.1 for the B10 Level 2 algorithm. • Improved spectroscopy for Oxygen A-band results in reduced spatial variability in surface pressure retrieval bias. • Orbiting Carbon Observatory retrievals are sensitive to changes to the water vapor continuum model. The accuracy of atmospheric trace gas retrievals depends directly on the accuracy of the molecular absorption model used within the retrieval algorithm. For remote sensing of well-mixed gases, such as carbon dioxide (CO 2), where the atmospheric variability is small compared to the background, the quality of the molecular absorption model is key. Recent updates to oxygen (O 2) absorption coefficients (ABSCO) for the 0.76 µm A-band and the water vapor (H 2 O) continuum model within the 1.6 µm and 2.06 µm CO 2 bands used within the Orbiting Carbon Observatory (OCO-2 and OCO-3) algorithm are described here. Updates in the O 2 A-band involve the inclusion of new laboratory measurements within multispectrum fits to improve relative consistency between O 2 line shapes and collision-induced absorption (CIA). The H 2 O continuum model has been updated to MT _ CKD v3.2, which has benefited from information from a range of laboratory studies relative to the model utilized in the previous ABSCO version. Impacts of these spectroscopy updates have been evaluated against ground-based atmospheric spectra from the Total Carbon Column Observing Network (TCCON) and within the framework of the OCO-2 algorithm, using OCO-2 soundings covering a range of atmospheric and surface conditions. The updated absorption coefficients (ABSCO version 5.1) are found to offer improved fitting residuals and reduced biases in retrieved surface pressure relative to the previous version (ABSCO v5.0) used within B8 and B9 of the OCO-2 retrieval algorithm and have been adopted for the OCO B10 Level 2 algorithm. [ABSTRACT FROM AUTHOR]

Cricothyrotomy proficiency is imperative for anesthesia providers; however, opportunities to perform this skill are infrequent making skill maintenance essential. Increased accessibility of three-dimensional (3D) printing allows for production of low-cost simulation models. The models used for simulation-based teaching and deliberate practice facilitate skill development and refinement. A cost-effective, 3D printed airway model was designed and allowed 47 anesthesia providers to complete hands-on practice during a cricothyrotomy simulation-based training program. Assessment and comparison of pre- to post- intervention was completed for self-efficacy, knowledge, time to skill completion, compliance of required steps checklist, Global Rating Scale score, and successful ventilation. Statistically significant improvement from pre- to posttest was demonstrated in: 1) self-efficacy survey scores (P < .001); 2) knowledge test scores (P < .001); and 3) decreased time (minutes:seconds) to task completion (P < .001). Time from initial posttest to 3-month posttest (P = .046) significantly increased, however, the time at 3 months posttest remained significantly lower than pretest (P < .001). Providers' ability to ventilate, compliance with procedural steps, and technical skills significantly improved. 3D printing can produce anatomically similar simulation airway models that allow providers to practice and improve cricothyrotomy knowledge and skills. Developing an affordable and accessible simulation model provides a sustainable tool that allows providers multiple cricothyrotomy practice attempts., Competing Interests: Name: Felicia Hogan, DNP, CRNA Contribution: This author made significant contributions to the conception, synthesis, writing, and final editing and approval of the manuscript to justify inclusion as an author. Disclosures: None. Name: Christopher O’Dell Contribution: This author made significant contributions to the conception, synthesis, writing, and final editing and approval of the manuscript to justify inclusion as an author. Disclosures: None. Name: Julie Pearson, PhD, CRNA Contribution: This author made significant contributions to the conception, synthesis, writing, and final editing and approval of the manuscript to justify inclusion as an author. Disclosures: None. Name: Joseph Parrish, MSN, CRNA Contribution: This author made significant contributions to the conception, synthesis, writing, and final editing and approval of the manuscript to justify inclusion as an author. Disclosures: None. Name: Virginia C. Simmons, DNP, CRNA, CHSE-A, FAANA, FAAN Contribution: This author made significant contributions to the conception, synthesis, writing, and final editing and approval of the manuscript to justify inclusion as an author. Disclosures: None, (Copyright © by the American Association of Nurse Anesthetists.)

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO 2 ) emissions and on the removal of CO 2 by land carbon sinks. China is currently the single largest emitter of CO 2 , responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 2017 1 . Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage 2-4 , which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO 2 , measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of -1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions 5,6 , with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper 7 . Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions.

The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently provides 29 years (1988 - 2016) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) inter-calibrated 0.25°-resolution retrievals. Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2 and GMI. To mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and monthly-mean diurnal cycle. In addition to a longer record and six additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrievals, a correction for a CLWP bias (based on matchups to clear-sky MODIS scenes), and the construction of a total (cloud+rain) liquid water path (TLWP) record for use in analyses of columnar liquid water in raining clouds. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TLWP field can also be used as a quality-control screen, where uncertainty increases as the ratio of CLWP to TLWP decreases. For regions where confidence in CLWP is highest (i.e. CLWP:TLWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from -15% (e.g. global oceanic stratocumulus decks) to +5-10% (e.g. portions of the higher-latitudes, storm tracks, and shallower convection regions straddling the ITCZ). The dataset is currently hosted at the Goddard Earth Science Data and Information Services Center (http://disc.sci.gsfc.nasa.gov).

The 2015-2016 El Niño led to historically high temperatures and low precipitation over the tropics, while the growth rate of atmospheric carbon dioxide (CO 2 ) was the largest on record. Here we quantify the response of tropical net biosphere exchange, gross primary production, biomass burning, and respiration to these climate anomalies by assimilating column CO 2 , solar-induced chlorophyll fluorescence, and carbon monoxide observations from multiple satellites. Relative to the 2011 La Niña, the pantropical biosphere released 2.5 ± 0.34 gigatons more carbon into the atmosphere in 2015, consisting of approximately even contributions from three tropical continents but dominated by diverse carbon exchange processes. The heterogeneity of the carbon-exchange processes indicated here challenges previous studies that suggested that a single dominant process determines carbon cycle interannual variability., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

In this study, observed cloud liquid water path (LWP) trends from the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset (1988 - 2014) are compared to trends computed from the temporally-coincident records of 16 global climate models (GCMs) participating in the Coupled Model Intercomparison Project 5 (CMIP5). For many regions, observed trend magnitudes are several times larger than the corresponding model mean trend magnitudes. Muted model mean trends are thought to be the result of cancellation effects arising from differing interannual variability characteristics and differences in model physics/dynamics. In most regions, the majority of modeled trends were statisically consistent with the observed trends. This was thought to be because of large estimated errors in both the observations and the models due to interannual variability. Over the southern oceans (south of 40S latitude), general agreement between the observed trend and virtually all GCM trends is also found (~ 1-2 g m -2 decade -1 ). Observed trends are also compared to those from the atmospheric model intercomparison project (AMIP). Like the CMIP5 models, the majority of modeled AMIP trends were statistically consistent with the observed trends. It was also found that, in regions where the AMIP model mean time series better captures observed interannual variability, it tends to better capture the magnitude of the observed trends.