10 years of temperature and wind observation on a 45 m tower at Dome C, East Antarctic plateau

Long-term, continuous in situ observations of the near-surface atmospheric boundary layer are critical for many weather and climate applications. Although there is a proliferation of surface stations globally, especially in and around populous areas, there are notably fewer tall meteorological towers with multiple instrumented levels. This is particularly true in remote and extreme environments such as the East Antarctic plateau. In the article, we present and analyze 10 years of data from six levels of meteorological instrumentation mounted on a 42 m tower located at Dome C, East Antarctica, near the Concordia research station, producing a unique climatology of the near-surface atmospheric environment (Genthon et al., 2021a, b). Monthly temperature and wind data demonstrate the large seasonal differences in the near-surface boundary layer dynamics, depending on the presence or absence of solar surface forcing. Strong vertical temperature gradients (inversions) frequently develop in calm, winter conditions, while vertical convective mixing occurs in the summer, leading to near-uniform temperatures along the tower. Seasonal variation in wind speed is much less notable at this location than the temperature variation as the winds are less influenced by the solar cycle; there are no katabatic winds as Dome C is quite flat. Harmonic analysis confirms that most of the energy in the power spectrum is at diurnal, annual and semi-annual timescales. Analysis of observational uncertainty and comparison to reanalysis data from the latest generation of ECMWF (European Centre for Medium-Range Weather Forecasts) reanalyses (ERA5) indicate that wind speed is particularly difficult to measure at this location. Data are distributed on the PANGAEA data repository at https://doi.org/10.1594/PANGAEA.932512 (Genthon et al., 2021a) and https://doi.org/10.1594/PANGAEA.932513 (Genthon et al., 2021b).

A Numerical Study of the Solar Modulation of Galactic Protons and Helium from 2006 to 2017

With continuous measurements from space-borne cosmic-ray detectors such as AMS-02 and PAMELA, precise spectra of galactic cosmic rays over the 11 yr solar cycle have become available. For this study, we utilize proton and helium spectra below 10 GV from these missions from 2006 to 2017 to construct a cosmic-ray transport model for a quantitative study of the processes of solar modulation. This numerical model is based on Parker’s transport equation, which includes four major transport processes. The Markov Chain Monte Carlo method is utilized to search the relevant parameter space related to the drift and the diffusion coefficients by reproducing and fitting the mentioned observed spectra. The resulting best-fit normalized χ 2 is mainly less than 1. It is found that (1) when reproducing these observations the parameters required for the drift and diffusion coefficients exhibit a clear time dependence, with the magnitude of the diffusion coefficients anticorrelated with solar activity; (2) the rigidity dependence of the resulting mean free paths varies with time, and their rigidity dependence at lower rigidity can even have a larger slope than at higher rigidity; (3) using a single set of modulation parameters for each pair of observed proton and helium spectra, most spectra are reproduced within observational uncertainty; and (4) the simulated proton-to-helium flux ratio agrees with the observed values in terms of its long-term time dependence, although some discrepancy exists, and the difference is mostly coming from the underestimation of proton flux.

Magnetic and Velocity Fluctuations in the Near-Sun Region from 0.1−0.3 au Observed by Parker Solar Probe

The fluctuations observed in the slow solar wind at 1 au by the WIND spacecraft are shown by recent studies to consist of mainly magnetic-field directional turning and magnetic-velocity alignment structure (MVAS). How these structures are created has been a question because the nature of the fluctuations in the near-Sun region remains unknown. Here, we present an analysis of the measurements in the slow solar wind from 0.1−0.3 au by Parker Solar Probe during its first six orbits. We present the distributions in the C vb ′ – σ r plane of both the occurrence and average amplitudes of the fluctuations, including the magnetic field, the velocity, and the Elsässer variables, where C vb ′ is the correlation coefficient between the magnetic and velocity fluctuations multiplied by the opposite sign of the radial component of the mean magnetic field and σ r is the normalized residual energy. We find that the dominant composition is the outward-propagating Alfvénic fluctuations. We find Alfvénic fluctuations with C vb ′ > 0.95 , in which the amplitudes of z + reach 60 km s−1 and those of z − are close to the observational uncertainty. We also find a region with high C vb ′ and moderate minus σ r in which the fluctuations are considered MVAS being magnetic dominated with the amplitude of magnetic fluctuations reaching 60 km s−1. We provide empirical relations between the velocity fluctuation amplitude and C vb ′ . The comparison between these results and those observed at 1 au may provide some clues as to the nature and evolution of the fluctuations.

Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is one of the critical and severe neurological diseases with high morbidity and mortality. Head computed tomography (CT) is among the preferred methods for the diagnosis of SAH, which is confirmed by CT showing high-density shadow in the subarachnoid space. Analysis of these images through a deep learning-based subarachnoid hemorrhage will reduce the approximate rate of misdiagnosis in general and missed diagnosis by clinicians in particular. Deep learning-based detection of subarachnoid hemorrhage mainly includes two tasks, i.e., subarachnoid hemorrhage classification and subarachnoid hemorrhage region segmentation. However, it is difficult to effectively judge reliability of the model and classify bleeding which is based on limited predictive probability of convolutional neural network output. Moreover, deep learning-based bleeding area segmentation requires a large amount of training data to be marked in advance and the large number of network parameters makes the model training unable to reach the optimal. To resolve these problems associated with existing models, Bayesian deep learning and neural network-based hybrid model is presented in this paper to estimate uncertainty and efficiently classify subarachnoid hemorrhage. Uncertainty estimation of the proposed model helps in judging whether the model’s prediction is reliable or not. Additionally, it is used to guide clinicians to find the neglected subarachnoid hemorrhage area. In addition, a teacher-student mechanism deep learning model was designed to introduce observational uncertainty estimation for semisupervised learning of subarachnoid hemorrhage. Observation uncertainty estimation detects the uncertain bleeding areas in CT images and then selects areas with high reliability. Finally, it uses these unlabeled data for model training purposes as well.

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

A derivative-free optimisation method for global ocean biogeochemical models

The performance of global ocean biogeochemical models, and the Earth System Models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model-data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, inorganic phosphate and inorganic nitrate observations from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers 5 of the 6 parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000 year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a baseline misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality Application to the United States Historical Record

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. We use output from global climate models in a perfect-data sense to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day). In doing so, we turn an apparent limitation into an opportunity by using the diversity of responses to short-lived climate forcers across the CMIP6 multi-model ensemble to ensure our D&A is insensitive to structural uncertainty. Our framework is developed using a Pearl-causal perspective, but forthcoming research now underway will apply the framework to in situ measurements using a Granger-causal perspective. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

Probabilistic projections of future warming and climate sensitivity trajectories

Projections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9 °C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2 °C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5 °C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2 °C warming.

Double Differencing by Demeaning: Applications to Hypocenter Location and Wavespeed Tomography

ABSTRACT Double differencing of body-wave arrival times has proved to be a useful technique for increasing the resolution of earthquake locations and elastic wavespeed images, primarily because (1) differences in arrival times often can be determined with much greater precision than absolute onset times and (2) differencing reduces the effects of unknown, unmodeled, or otherwise unconstrained variables on the arrival times, at least to the extent that those effects are common to the observations in question. A disadvantage of double differencing is that the system of linearized equations that must be iteratively solved generally is much larger than the undifferenced set of equations, in terms of both the number of rows and the number of nonzero elements. In this article, a procedure based on demeaning subsets of the system of equations for hypocenters and wavespeeds that preserves the advantages of double differencing is described; it is significantly more efficient for both wavespeed-only tomography and joint hypocenter location-wavespeed tomography. Tests suggest that such demeaning is more efficient than double differencing for hypocenter location as well, despite double-differencing kernels having fewer nonzeros. When these subsets of the demeaned system are appropriately scaled and simplified estimates of observational uncertainty are used, the least-squares estimate of the perturbations to hypocenters and wavespeeds from demeaning are identical to those obtained by double differencing. This equivalence breaks down in the case of general, observation-specific weighting, but tests suggest that the resulting differences in least-squares estimates are likely to be inconsequential. Hence, demeaning offers clear advantages in efficiency and tractability over double differencing, particularly for wavespeed tomography.

Precipitation over southern Africa: Is there consensus among GCMs, RCMs and observational data?

The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is projected to experience severe precipitation shortages in the coming decades. Ensuring that our modelling tools are fit for the purpose of assessing these changes is critical. In this work we compare a range of satellite products along with gauge-based datasets. Additionally, we investigate the behaviour of regional climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) – Africa domain, along with simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6). We identify considerable variability in the standard deviation of precipitation between satellite products that merge with rain gauges and satellite products that do not, during the rainy season (Oct–Mar), indicating high observational uncertainty for specific regions over SAF. Good agreement both in spatial pattern and the strength of the calculated trends is found between satellite and gauge-based products, however. Both CORDEX-Africa and CMIP5 ensembles underestimate the observed trends during the analysis period. The CMIP6 ensemble displayed persistent drying trends, in direct contrast to the observations. The regional ensemble exhibited improved performance compared to its forcing (CMIP5), when the annual cycle and the extreme precipitation indices were examined, confirming the added value of the higher resolution regional climate simulations. The CMIP6 ensemble displayed a similar behaviour to CMIP5, however reducing slightly the ensemble spread. However, we show that reproduction of some key SAF phenomena, like the Angolan Low (which exerts a strong influence on regional precipitation), still poses a challenge for the global and regional models. This is likely a result of the complex climatic process that take place. Improvements in observational networks (both in-situ and satellite), as well as continued advancements in high-resolution modelling will be critical, in order to develop a robust assessment of climate change for southern Africa.