ALBERT

Description: Atmospheric black carbon (BC) aerosol, as the most significant light absorbing aerosol component, has been a long-lasting uncertain component in environmental and climate studies. Taking the recent IPCC report as an example, the estimated effective radiative forcing due to BC emission is between -0.28 and 0.42 W m〈sup〉-2〈/sup〉. However, current global climate models struggle to accurately represent detailed aerosol microphysical and mixing properties due to computational limitations, which would certainly result in biases on their optical properties and radiative effects. One of the most unsettled problems is that the modeled BC absorption enhancement is much larger than in-situ observations, and this could be caused by the poor representation of BC properties in GCMs. Meanwhile, various observations have been provided some fundamental BC properties in ambient atmospheres, e.g., their typical size distributions and mixing properties. This study implements much reasonable BC properties into the CESM-CAM6 to better represent their optical properties, and intends to improve our understanding on their optical and radiative properties. Our results indicate that the new model shows improved agreement with in-situ measurements of BC absorption enhancement, and currently may overestimate BC radiative effectives by over 20%. Additionally, our new scheme may be easily applied in GCMs with similar aerosol module.

Description: Black carbon (BC) plays an important role in the climate system due to its strong warming effect, yet the magnitude of this effect is highly uncertain due to the complex mixing state of aerosols. Here we build a unified theoretical framework to describe BC’s mixing states, linking dynamic processes to BC coating thickness distribution, and show its self-similarity for sites in diverse environments. A new mixing state module is established based on this finding and successfully applied in global and regional models, which increases the accuracy of aerosol climate effect estimations.

Description: Tree-ring chronologies underpin the majority of annually-resolved reconstructions of Common Era climate. However, they are derived using different datasets and techniques, the ramifications of which have hitherto been little explored. Here, we report the results of a double-blind experiment that yielded 15 Northern Hemisphere summer temperature reconstructions from a common network of regional tree-ring width datasets. Taken together as an ensemble, the Common Era reconstruction mean correlates with instrumental temperatures from 1794–2016 CE at 0.79 (p 〈 0.001), reveals summer cooling in the years following large volcanic eruptions, and exhibits strong warming since the 1980s. Differing in their mean, variance, amplitude, sensitivity, and persistence, the ensemble members demonstrate the influence of subjectivity in the reconstruction process. We therefore recommend the routine use of ensemble reconstruction approaches to provide a more consensual picture of past climate variability.

Description: Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Description: This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Description: The 13C isotopic ratio of methane, δ13C of CH4, provides additional constraints on the CH4 budget to complement the constraints from CH4 observations. The interpretation of δ13C observations is complicated, however, by uncertainties in the methane sink. The reaction of CH4 with Cl is highly fractionating, increasing the relative abundance of 13CH4, but there is currently no consensus on the strength of the tropospheric Cl sink. Global model simulations of halogen chemistry differ strongly from one another in terms of both the magnitude of tropospheric Cl and its geographic distribution. This study explores the impact of the intermodel diversity in Cl fields on the simulated δ13C of CH4. We use a set of GEOS global model simulations with different predicted Cl fields to test the sensitivity of the δ13C of CH4 to the diversity of Cl output from chemical transport models. We find that δ13C is highly sensitive to both the amount and geographic distribution of Cl. Simulations with Cl providing 0.28 % or 0.66 % of the total CH4 loss bracket the δ13C observations for a fixed set of emissions. Thus, even when Cl provides only a small fraction of the total CH4 loss and has a small impact on total CH4, it provides a strong lever on δ13C. Consequently, it is possible to achieve a good representation of total CH4 using widely different Cl concentrations, but the partitioning of the CH4 loss between the OH and Cl reactions leads to strong differences in isotopic composition depending on which model's Cl field is used. Comparing multiple simulations, we find that altering the tropospheric Cl field leads to approximately a 0.5 ‰ increase in δ13CH4 for each percent increase in how much CH4 is oxidized by Cl. The geographic distribution and seasonal cycle of Cl also impacts the hemispheric gradient and seasonal cycle of δ13C. The large effect of Cl on δ13C compared to total CH4 broadens the range of CH4 source mixtures that can be reconciled with δ13C observations. Stronger constraints on tropospheric Cl are necessary to improve estimates of CH4 sources from δ13C observations.

Description: Emission inventories are a critical basis for air quality and climate modeling, as well as policy decisions. Non-methane volatile organic compounds (NMVOCs) are key precursor compounds in ozone and secondary organic aerosol formation. Accurately representing NMVOCs in emission inventories is crucial for understanding atmospheric chemistry, the impact of policy measures, and climate projections. Improving NMVOC representation in emission inventories is fraught with challenges, ranging from the lack of (long-term) NMVOC measurements, limited efforts in updating emission factors, to the diversity of NMVOC species reactivity. Here we take an initial step to evaluate the representation of urban NMVOC speciation in an emission inventory (EDGARv4.3.2 and EDGARv6.1) at the global level. To compare the urban measurements of NMVOCs to the emission inventory estimates, ratios of individual NMVOCs to acetylene are used. Owing to limitations in measurement data and grouping of NMVOCs in emission inventories, the comparison includes only a limited number of alkanes, alkenes, and aromatics. Results show little to no agreement between the ratios in the observations and those in the global emission inventory for the species compared (r2 0.01–0.20). This could be related to incorrect speciation profiles and/or spatial allocation of NMVOCs to urban areas. Regional emission inventories show better agreement among the ratios (r2 0.43–0.70). The inclusion of oxygenated species in NMVOC measurements, as well as greater global coverage of measurements could improve representation of NMVOC species in emission inventories, and a mosaic of regional inventories may be a better approach.

Description: The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere with air masses showing chemical and dynamical properties of both regions. The representation of the TTL in meteorological reanalysis data sets is important for studying the complex interactions of circulation, convection, trace gases, clouds, and radiation. In this paper, we present the evaluation of climatological and long-term TTL temperature and tropopause characteristics in the reanalysis data sets ERA-Interim, ERA5, JRA-25, JRA-55, MERRA, MERRA-2, NCEP-NCAR (R1), and CFSR. The evaluation has been performed as part of the SPARC (Stratosphere–troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The most recent atmospheric reanalysis data sets (ERA-Interim, ERA5, JRA-55, MERRA-2, and CFSR) all provide realistic representations of the major characteristics of the temperature structure within the TTL. There is good agreement between reanalysis estimates of tropical mean temperatures and radio occultation data, with relatively small cold biases for most data sets. Temperatures at the cold point and lapse rate tropopause levels, on the other hand, show warm biases in reanalyses when compared to observations. This tropopause-level warm bias is related to the vertical resolution of the reanalysis data, with the smallest bias found for data sets with the highest vertical resolution around the tropopause. Differences in the cold point temperature maximize over equatorial Africa, related to Kelvin wave activity and associated disturbances in TTL temperatures. Interannual variability in reanalysis temperatures is best constrained in the upper TTL, with larger differences at levels below the cold point. The reanalyses reproduce the temperature responses to major dynamical and radiative signals such as volcanic eruptions and the quasi-biennial oscillation (QBO). Long-term reanalysis trends in temperature in the upper TTL show good agreement with trends derived from adjusted radiosonde data sets indicating significant stratospheric cooling of around −0.5 to −1 K per decade. At 100 hPa and the cold point, most of the reanalyses suggest small but significant cooling trends of −0.3 to −0.6 K per decade that are statistically consistent with trends based on the adjusted radiosonde data sets. Advances of the reanalysis and observational systems over the last decades have led to a clear improvement in the TTL reanalysis products over time. Biases of the temperature profiles and differences in interannual variability clearly decreased in 2006, when densely sampled radio occultation data started being assimilated by the reanalyses. While there is an overall good agreement, different reanalyses offer different advantages in the TTL such as realistic profile and cold point temperature, continuous time series, or a realistic representation of signals of interannual variability. Their use in model simulations and in comparisons with climate model output should be tailored to their specific strengths and weaknesses.

Description: Atmospheric carbon dioxide (CO2) inversions for estimating natural carbon fluxes typically do not allow for adjustment of fossil fuel CO2 emissions, despite significant uncertainties in emission inventories and inadequacies in the specification of international bunker emissions in inversions. Also, most inversions place CO2 release from fossil fuel combustion and biospheric sources entirely at the surface. However, a non-negligible portion of the emissions actually occurs in the form of reduced carbon species, which are eventually oxidized to CO2 downwind. Omission of this 'chemical pump' can result in a significant redistribution of the inferred total carbon fluxes among regions. We assess the impacts of different prescriptions of fossil fuel emissions and accounting for the chemical pump on flux estimation, with a novel aspect of conducting both satellite CO2 observation-based and surface in situ-based inversions. We apply 3-D carbon monoxide (CO) loss rates archived from a state-of-the-art GEOS chemistry and climate model simulation in a forward transport model run to simulate the distribution of CO2 originating from oxidation of carbon species. We also subtract amounts from the prior surface CO2 fluxes that are actually emitted in the form of fossil and biospheric CO, methane, and non-methane volatile organic compounds (VOCs). We find that the posterior large-scale fluxes are generally insensitive to the finer-scale spatial differences between the ODIAC and CDIAC fossil fuel CO2 gridded datasets and assumptions about international bunker emissions. However, accounting for 3-D chemical CO2 production and the surface correction shifts the global carbon sink, e.g. from land to ocean and from the tropics to the north, with a magnitude and even direction that depend on assumptions about the surface correction. A GOSAT satellite-based inversion is more sensitive to the chemical pump than one using in situ observations, exhibiting substantial flux impacts of 0.28, 0.53, and −0.47 Pg C yr−1 over tropical land, global land, and oceans, due to differences in the horizontal and vertical sampling of the two observation types. Overall, the biases from neglecting the chemical pump appear to be minor relative to the flux estimate uncertainties and the differences between the in situ and GOSAT inversions, but their relative importance will grow in the future as observational coverage further increases and satellite retrieval biases decrease.

Description: A correction to this paper has been published: 10.1140/epjc/s10052-021-09344-w