Upward lightning (UL) has become a major threat to the growing number of wind turbines producing renewable electricity. It can be much more destructive than downward lightning due to the large charge transfer involved in the discharge process. Ground-truth lightning current measurements indicate that less than 50% of UL could be detected by lightning location systems (LLS). UL is expected to be the dominant lightning type during the cold season. However, current standards for assessing the risk of lightning at wind turbines mainly consider summer lightning, which is derived from LLS. This study assesses the risk of LLS-detectable and LLS-undetectable UL at wind turbines using direct UL measurements at instrumented towers. These are linked to meteorological data using random forests. The meteorological drivers for the absence/occurrence of UL are found from these models. In a second step, the results of the tower-trained models are extended to a larger study area (central and northern Germany). The tower-trained models for LLS-detectable lightning are independently verified at wind turbine sites in this area and found to reliably diagnose this type of UL. Risk maps based on cold season case study events show that high probabilities in the study area coincide with actual UL flashes. This lends credibility to the application of the model to all UL types, increasing both risk and affected areas.

Upward lightning is rarer than downward lightning and requires tall (100+m) structures to initiate. It may be either self-initiated or triggered by other lightning discharges. While conventional lightning location systems (LLSs) detect most of the upward lightning flashes superimposed by pulses or return strokes, they miss a specific flash type that consists only of a continuous current. Globally, only few specially instrumented towers can record this flash type. The proliferation of wind turbines in combination with damages from upward lightning necessitates an improved understanding under which conditions self-initiated upward lightning and the continuous-current-only subtype occur. This study uses a random forest machine learning model to find the larger-scale meteorological conditions favoring the occurrence of the different phenomena. It combines ground truth lightning current measurements at the specially instrumented tower at Gaisberg mountain in Austria with variables from larger-scale meteorological reanalysis data (ERA5). These variables reliably explain whether upward lightning is self-initiated or triggered by other lightning discharges. The most important variable is the height of the -10°C isotherm above the tall structure: the closer it is, the higher is the probability of self-initiated upward lightning. For the different flash types, this study finds a relationship to the larger-scale electrification conditions and the LLS-detected lightning situation in the vicinity. Lower amounts of supercooled liquid water, solid, and liquid differently sized particles and no LLS-detected lightning events nearby favor the continuous-current-only subtype compared to the other subtypes, which preferentially occur with LLS-detected lightning events within 3km from the Gaisberg Tower.

Variations in atmosphere total column-mean CO2 (XCO2) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO2 from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO2 reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO2 variability in addition to quantifying uncertainty in XCO2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO2. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.

Uncertainties of regional precipitation projections are substantial, and users of such projections face the so-called practitioners dilemma: a plethora of projections with different models from different ensembles of different types and generations are available. But the consistency of these projections has not been systematically assessed, such that no clear guidance about the use of these ensembles exists. Therefore, we systematically compare, separately for each season, projections of mean precipitation and extremes of daily precipitation over Europe across a wide range of climate model ensembles. We specifically consider the global climate model ensembles CMIP3, CMIP5, Coupled Model Intercomparison Project Phase 6 (CMIP6), and HighresMIP as well as the regional climate model ensembles ENSEMBLES and EURO-CORDEX. All considered ensembles agree in their large-scale patterns of changes for both mean and extreme daily precipitation, but at the regional scale, substantial discrepancies and inconsistencies are evident. Within and across ensemble spread is strongest in summer, in particular for the drying trend over the Mediterranean and Eastern Europe. CMIP5 and CMIP6 are broadly consistent. The regional climate model (RCM) ensembles modify the signals of the driving global climate models indicating potential added value. The high resolution of the RCM and HighresMIP ensembles seems to be key over the Alps for summer precipitation. Our study provides important information for users of climate projections as it helps to establish continuity across generations and types of climate models, and aids the design of new climate impact studies.

Heavy rainfall is a challenge to forecast due to the variety of rainfall intensities and durations across a wide spectrum of high-impact storm types. In this study, we analyze extreme storms in Tropical and Subtropical East Asia, a moisture-rich environment with complex terrain and oceanic regions. The Tropical Rainfall Measuring Mission's Precipitation Radar is utilized to characterize the frequency and rainfall intensity of four extreme storm types. Extreme storms producing heavy precipitation are categorized into four types: deep convective cores (DCCs), deepwide convective cores (DWCCs), wide convective cores (WCCs), and broad stratiforms regions (BSRs). DCCs and DWCCs occur more frequently and produce stronger rain intensities over land compared to those over ocean. However, WCCs and BSRs occur more frequently over oceans, especially in association with the Meiyu front season and climatological progression in the northern subregions. Although the Convective Cores show higher rain intensities than the BSRs, they show lower volumetric rain rate due to their comparatively smaller horizontal area. An ingredients-based framework is applied to find key similarities across the different heavy rainfall-producing storms near Taiwan using ERA5 reanalysis. The analysis shows that the broader systems (i.e., WCCs and BSRs) are associated with larger in area and longer timescales of vertical moisture flux and low-level wind shear that support the development of the horizontally large, organized storms. Smaller DCCs do not show strong vertical moisture flux on the spatial scales resolved by the reanalysis, suggesting their more local nature and less meso- or synoptic scale support.

Two persistent and heavy haze episodes during the COVID-19 lockdown (from 20 Jan to 22 Feb 2020) still occur in northern China, when anthropogenic emissions, particularly from transportation sources, are greatly reduced. To investigate the underlying cause, this study comprehensively uses in-situ measurements for ambient surface pollutants, reanalysis meteorological data and the WRF-Chem model to calculate the contribution of NOx emission change and weather-climate change to the "unexpectedly heavy" haze. Results show that a substantial NOx reduction has slightly decreased PM2.5 concentration. By contrast, the weakest East Asian winter monsoon (EAWM) in the 2019-2020 winter relative to the past decade is particularly important for haze occurrence. A warmer and moister climate is also favorable. Model results suggest that climate anomalies lead to a 25-50μgm-3 increase of PM2.5 concentration, and atmospheric transport is also an important contributor to two haze episodes. The first haze is closely related to the atmospheric transport of pollutants from NEC to the south, and fireworks emissions in NEC are a possible amplifying factor that warrants future studies. The second one is caused by the convergence of a southerly wind and a mountain wind, resulting in an intra-regional transport within BTH, with a maximal PM2.5 increment of 50-100μgm-3. These results suggest that climate change and regional transport are of great importance to haze occurrence in China, even with significant emission reductions of pollutants.

Different functions are used to account for turbulence strength in the atmospheric boundary layer for different stability regimes. These functions are one of the sources for differences among different atmospheric models' predictions and associated biases. Also, turbulence strength is underrepresented in some of the resistance formulations. To address these issues with dry deposition, firstly we take advantage of three-dimensional (3-D) turbulence information in estimating resistances by proposing and validating a 3-D turbulence velocity scale that is relevant for different stability regimes of boundary layer. Secondly, we hypothesize and validate that friction velocity measured by 3-D sonic anemometer can be effectively replaced by the new turbulence velocity scale multiplied by the von Karman constant. Finally, we (1) present a set of resistance formulations for ozone (O3) based on the 3-D turbulence velocity scale; (2) intercompare estimations of such resistances with those obtained using existing formulations; and, (3) evaluate simulated O3 fluxes using a single-point dry deposition model against long-term observations of O3 fluxes at the Harvard Forest (MA) site. Results indicate that the new resistance formulations work very well in simulating surface latent heat and O3 fluxes when compared to respective existing formulations and measurements at a decadal time scale. Findings from this research may help to improve the capability of dry deposition schemes for better estimation of dry deposition fluxes and create opportunities for the development of a community dry deposition model for use in regional/global air quality models.

The Brewer-Dobson Circulation (BDC) determines the distribution of long-lived tracers in the stratosphere; therefore, their changes can be used to diagnose changes in the BDC. We evaluate decadal (2005-2018) trends of nitrous oxide (N2O) in two versions of the Whole Atmosphere Chemistry-Climate Model (WACCM) by comparing them with measurements from four Fourier transform infrared (FTIR) ground-based instruments, the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and with a chemistry-transport model (CTM) driven by four different reanalyses. The limited sensitivity of the FTIR instruments can hide negative N2O trends in the mid-stratosphere because of the large increase in the lowermost stratosphere. When applying ACE-FTS measurement sampling on model datasets, the reanalyses from the European Center for Medium Range Weather Forecast (ECMWF) compare best with ACE-FTS, but the N2O trends are consistently exaggerated. The N2O trends obtained with WACCM disagree with those obtained from ACE-FTS, but the new WACCM version performs better than the previous above the Southern Hemisphere in the stratosphere. Model sensitivity tests show that the decadal N2O trends reflect changes in the stratospheric transport. We further investigate the N2O Transformed Eulerian Mean (TEM) budget in WACCM and in the CTM simulation driven by the latest ECMWF reanalysis. The TEM analysis shows that enhanced advection affects the stratospheric N2O trends in the Tropics. While no ideal observational dataset currently exists, this model study of N2O trends still provides new insights about the BDC and its changes because of the contribution from relevant sensitivity tests and the TEM analysis.

Clouds and blowing snow (BLSN) occur frequently over Antarctica, where it is critical to understand their feedbacks to surface and atmospheric boundary layer processes. Dome C, an elevated East Antarctic station, dominated by lengthy periods of surface longwave (LW) radiative cooling, is selected to reveal cloud and BLSN impacts within a largely stable environment. The sky condition is classified as clear, cloudy, or BLSN, using 3years of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite data. Co-located and contemporaneous in situ observations are used to investigate the relationship of sky condition with surface and atmospheric boundary layer thermal structure, focusing on seasonal variability. Results show that increased downwelling LW radiation from clouds abate surface radiative cooling losses, contributing to warming during all seasons. An increase of 3°C in the mean surface air temperature is observed during spring, whereas, a more dramatic rise (around 10°C), due to accompanying large-scale subsidence, is observed during fall and winter in association with clouds. For all seasons, the wind speed and wind speed shear are strongest during BLSN events, and the surface-based inversion is weakened by cooling which peaks in a shallow above-surface turbulent layer. The stronger background stability during fall and winter seasons, restricts turbulence and BLSN depths generally to the lowest tens of meters. The Earth's cryosphere is among the most rapidly evolving yet least well-observed regions, and knowledge of clouds and BLSN interactions with the typical stable atmospheric boundary layer can help further understand energy and moisture exchanges.

Open questions about the modulation of near-surface trace gas variability by stratosphere-troposphere tracer transport complicate efforts to identify anthropogenic sources of gases such as CFC-11 and N2O and disentangle them from dynamical influences. In this study, we explore one model's modulation of lower stratospheric tracer advection by the quasi-biennial oscillation (QBO) of stratospheric equatorial zonal-mean zonal winds at 50hPa. We assess instances of coherent modulation versus disruption through phase unlocking with the seasonal cycle in the model and in observations. We quantify modeled advective contributions to the temporal rate of change of stratospheric CFC-11 and N2O at extratropical and high-latitudes by calculating a transformed Eulerian mean (TEM) budget across isentropic surfaces from a 10-member WACCM4 ensemble simulation. We find that positive interannual variability in seasonal tracer advection generally occurs in the easterly QBO phase, as in previous work, and briefly discuss physical mechanisms. Individual simulations of the 10-member ensemble display phase-unlocking disruptions from this general pattern due to seasonally varying synchronizations between the model's repeating 28-month QBO cycle and the 12-month seasonal cycle. We find that phase locking and unlocking patterns of tracer advection calculations inferred from observations fall within the envelope of the ensemble member results. Our study bolsters evidence for variability in the interannual stratospheric dynamical influence of CFC-11 near-surface concentrations by assessing the QBO modulation of lower stratospheric advection via synchronization with the annual cycle. It identifies a likely cause of variations in the QBO influence on tropospheric abundances.