ALBERT

Description: The Observing Systems Simulation Experiment (OSSE) is a powerful approach to evaluate observing systems and data assimilation methods in numerical weather prediction (NWP). In this study, we extend the OSSE and design the Control Simulation Experiment (CSE), in which we add perturbations to the nature run (NR) and try to modify it to a desired state. Investigating what perturbations are effective to avoid a high-impact weather event would be useful to understand the controllability of such an event. Since the weather system is chaotic, and even more so for disturbances, small differences generally lead to big differences, particularly for high-impact weather events. This suggests potentially effective control, i.e., small interventions would lead to big differences for high-impact weather events. We first tested this idea with the Lorenz-63 3-variable model and found an effective approach to control the trajectory to stay in one side of the Lorenz’s butterfly attractor without shifting to the other. Next, we tested with the Lorenz-96 40-variable model to avoid the occurrences of extreme values, mimicking to avoid extreme events in NWP. Finally, we further extended the idea to test with realistic global and regional NWP models for a typhoon case and a local heavy rainfall case, respectively. This presentation will summarize the concept and methodology of CSE with some proof-of-concept demonstrations with the toy models and realistic NWP models. This is an attempt to a potential paradigm change of NWP research from decades of predictability to the new era of controllability.

Description: Earth’s climate experienced a major reorganization across the mid-Pleistocene transition (MPT) from 1.25 to 0.6 million years ago (Ma), when the dominant climate periodicity transitioned from 41-thousand years (kyr) to around 100-kyr. The MPT occurred without a concomitant shift in the orbital forcing rhythm, so it is related to internal climate dynamics rather than external astronomical forcing. Here, we investigate Asian climate dynamics associated with two extreme glacial loess coarsening events at the onset and middle of the MPT by combining new and existing grain size and magnetic susceptibility records from the Chinese Loess Plateau (CLP) spanning the last 1.6 Ma. We find that the two extreme glacial events were marked by a combination of intensified and expanded Asian aridity, winter monsoon strengthening, and distinct coarsening of loess layers L15 and L9-1 across the CLP. These two glacial intensifications coincided with notable Northern Hemisphere glacial ice sheet expansion at 1.25 and 0.9 Ma when the 100-kyr initiated and intensified. By integrating observations, land-sea correlations, and model simulations, we propose that these anomalously dry and windy Asian glacials were probably driven by an amplified terrestrial climate response to the coincident Northern Hemisphere ice sheet expansion. The shift from a 41-kyr to 100-kyr orbital periodicity across the MPT also occurred in our monsoon records, which reflect Northern Hemisphere ice sheet control on orbital-scale Asian climate variability, not just on extreme glacial Asian climate events at 1.25 and 0.9 Ma. Our study supports a close relationship between Asia-interior and global climate changes.

Description: While there is evidence for an acceleration in global mean sea-level (MSL) since the 1960s, its detection at local levels has been hampered by the considerable influence of natural variability on the rate of MSL change. Here we report an MSL acceleration in tide gauge records along the U.S. Southeast and Gulf coasts that has led to rates (〉10 mmyr-1 since 2010) that are unprecedented in at least 120 years. Using a variety of remote sensing and in-situ measurements in combination with climate model outputs, we show that this acceleration is primarily induced by an ocean dynamic signal exceeding the externally forced response from historical climate model simulations. However, when the simulated forced response is removed from observations, the residuals are neither historically unprecedented nor inconsistent with unforced variability in simulations. Furthermore, a large fraction of the residuals can be explained by remote wind forcing (via Rossby waves) over the tropical North Atlantic. This indicates that the acceleration represents the compounding effects of external forcing and internal climate variability.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr−1 (10.2±0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr−1, and SLAND was 3.8±0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade.

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.