JGR–Atmospheres

Large uncertainty exists in the sign of long-term changes in regional scale mean precipitation across the current generation of global climate models. To explore the physical drivers of this uncertainty for New Zealand, here we adopt a storyline approach applying cluster analysis to spatial patterns of future projected seasonal mean precipitation change across CMIP6 models (n = 43). For the winter precipitation change signal, the models split roughly into two main groups: both groups have a very robust wet signal across the west coast of the South Island but differ notably in terms of the sign of precipitation change across the north of the North Island. These far north winter precipitation differences appear related to how far the Hadley cell edge and regional eddy-driven jet shift across the models relative to their historical positions. In contrast, for summer, most models have a markedly weaker and spatially non-uniform response, where internal variability often plays a large role. However, a small group of models predict a robust wet signal across most of the country in summer. This “wet model” group is characterized by a regional La Niña-like increase in high pressure shifted further to the south-east of New Zealand, associated with more frequent north-easterly flow over the country and accompanied by significant warming of local sea surface temperatures. This regional circulation response appears related to changes in stationary Rossby wave paths as opposed to changes in La Niña occurrence frequency itself.

Accurate forecasting of atmospheric rivers (ARs) holds significance in preventing losses from extreme precipitation. However, traditional numerical weather prediction (NWP) models are computationally expensive and can be limited in accuracy due to inaccurate physical parameter settings. To overcome these limitations, we propose a deep learning (DL) model, called GAN-UNet, to forecast the AR occurrence, position, and intensity in East Asia. GAN-UNet can capture the complex nonlinear relationship between the inputs at the past moment, including the vertically integrated water vapor transport (IVT), zonal wind at 850 hPa (U850), and meridional wind at 850 hPa (V850), and the forecast output (IVT, U850, or V850), whose results are comparable to NWP models. In addition, the average model (AM) by integrating the results generated by GAN-UNet and European Centre for Medium-Range Weather Forecasts (ECMWF) outperforms all the NWP models selected in this study, demonstrating its potential to improve the performance of NWP through the DL method. Specifically, the 5-day average F1 scores of the AM are 0.777 and 0.845, whose values are significantly better than those obtained by ECMWF (0.712 and 0.794) in the two key regions of East Asia; The AM 5-day average intersection over unions are 0.706 and 0.688 while the values of ECMWF are 0.675 and 0.64; in terms of intensity forecast, GAN-UNet and AM exhibited lower differences in most of the intensity bins, except for the final bin with IVT more than 825 kg m−1 s−1. With this thorough analysis, GAN-UNet is shown as an effective model to forecast ARs.

The discovery of smoke-induced dynamical anomalies in the stratosphere associated with the 2019/2020 Australian New Year pyrocumulonimbus (pyroCb) super outbreak initiated a new field of study involving aerosol/weather anomalies. This paper documents the dynamical anomalies associated with the February 2009 Australian Black Saturday pyroCb outbreak. Positive potential vorticity anomalies (indicating anticyclonic rotation) with horizontal extent ∼1000 km and vertical thickness ∼2 km are associated with the plume, which we classify as a Smoke With Induced Rotation and Lofting (SWIRL). The SWIRL initially formed east of Australia, but then moved westward, crossing over Australia, and continuing to Africa. The SWIRL lasted for nearly three weeks (13 February–4 March), traveling ∼27,000 km and rising from potential temperatures of ∼410–500 K (altitudes ∼18–21 km). The altitude of the SWIRL is corroborated with coincident satellite-based profiles of H2O, CO, HCN, O3, and aerosol extinction. A vertical temperature dipole (±3 K) accompanied the PV anomaly, as verified with coincident Global Navigation Satellite System radio occultation temperatures. The SWIRL dissipated as it passed over Africa. Operational ECMWF forecasts with early initialization (13 February) and late initialization (21 February) are examined. In the early case, the forecasted PV anomaly disappeared within 4 days, as expected due to lack of smoke heating in the forecast model. In the late case, while the forecasted PV anomaly was weaker than in the reanalyzes, a remnant anomaly remained out to 10 days.

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long-term IA temporal variability remain unknown. Here, 1861 county-level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross-validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter-annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD-based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms.

Rossby Wave Packets (RWPs) are atmospheric perturbations located at upper levels in mid-latitudes which, in certain cases, terminate in Rossby Wave Breaking (RWB) events. When sufficiently persistent and spatially extended, these RWB events are synoptically identical to atmospheric blockings, which are linked to heatwaves and droughts. Thus, studying RWB events after RWPs propagation and their link with blocking is key to enhance extreme weather events detection 10–30 days in advance. Hence, here we assess (a) the occurrence of RWB events after the propagation of transient RWPs, (b) whether long-lived RWPs (RWPs with a lifespan above 8 days, or LLRWPs) are linked to large-scale RWB events that could form a blocking event, and (c) the proportion of blocking situations that occur near RWB events. To do so, we applied a tracking algorithm to detect transient RWPs in the southern hemisphere during summertime between 1979 and 2021, developed a wave breaking algorithm to identify RWB events, and searched for blocking events with different intensities. Results show that LLRWPs and the other RWPs displayed large-scale RWB events around 40% of the time, and most RWB events in both distributions last around 1–2 days, which is not long enough to identify them as blocking situations. Nearly 17% of blockings have a RWB event nearby, but barely 5% of blockings are linked to RWPs, suggesting that transient RWPs are not strongly linked to blocking development. Lastly, large-scale RWB events associated with RWPs that lasted less than 8 days are influenced by El Niño-Southern Oscillation.

Vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space-time intensified ABL observations, we evaluate the ABL-FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six-site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model-based result, so that a systematic comparison is conducted. Three physical processes involved in ABL-FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL-FT interface. Results show that the model-based and observation-based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL-FT exchange flux are quantified.

Turbulence in very stable boundary layers is typically unsteady and intermittent. The study implements a stochastic modeling approach to represent unsteady mixing possibly associated with intermittency of turbulence and with unresolved fluid motions such as dirty waves or drainage flows. The stochastic parameterization is introduced by randomizing the mixing lengthscale used in a Reynolds average Navier-Stokes (RANS) model with turbulent kinetic energy closure, resulting in a stochastic unsteady RANS model. The randomization alters the turbulent momentum diffusion and accounts for sporadic events of possibly unknown origin that cause unsteady mixing. The paper shows how the proposed stochastic parameterization can be integrated into a RANS model used in weather-forecasting and its impact is analyzed using neutrally and stably stratified idealized numerical case studies. The simulations show that the framework can successfully model intermittent mixing in stably stratified conditions, and does not alter the representation of neutrally stratified conditions. It could thus present a way forward for dealing with the complexities of unsteady flows in numerical weather prediction or climate models.

The zonal-mean subtropical westerly jet (SWJ) in boreal winter shows a significant northward shift trend under recent climate change. Previous studies proposed thermal forcing—represented by the thermal wind associated with the temperature gradient—and the driving effect of the eddy momentum flux (EMF) convergence that leads to the eddy-driven jet as explanations for this process; however, their relative importance in influencing the SWJ shift requires further investigation. In this study, we examined the roles of thermal forced and eddy-driven jet components in the zonal-mean northward SWJ shift. We also investigated the role of Hadley circulation because its poleward boundary is related to the zonal-mean SWJ. Results suggest that thermal forced component plays a major role, while EMF-driven component plays a secondary role. Specifically, the subtropical warming, which is primarily influenced by enhanced adiabatic downward motion of the Hadley circulation, increases the meridional temperature gradient and the associated thermal wind poleward of the SWJ. It also reduces atmospheric static stability aloft and converges the EMF on the poleward side of the climatological SWJ. Enhanced meridional temperature gradient and EMF convergence on the poleward side push the SWJ northward. Results from further mathematical analysis indicate that thermal forced and eddy-driven zonal wind components account for 72% and 28% of the shift distance, respectively. In addition to elucidating the relative importance of thermal and EMF forcings, this study emphasizes the critical role of the subtropical warming driven by the intensified local descending branch of the Hadley circulation in shifting the zonal-mean SWJ.

Changes in tropical cyclones due to greenhouse-gas forcing in the Shanghai area have been studied in a double-nesting regional model experiment using the Met Office convection-permitting model HadREM3-RA1T at 4 km resolution and the regional model HadREM3-GA7.05 at 12 km for the intermediate nest. Boundary conditions for the experiment have been constructed from HadGEM2-ES, a General Circulation Model (GCM) from the 5th Coupled Model Intercomparison Project (CMIP5), directly using high-frequency data for the atmosphere (6-hourly) and the ocean (daily), for the historical period (1981–2000) and under the Representative Concentration Pathway 8.5 (2080–2099). These choices identify one of the warmest climate scenarios available from CMIP5. Given the direct use of GCM data for the baseline, large scale conditions relevant for tropical cyclones have been analyzed, demonstrating a realistic representation of environmental conditions off the coast of eastern China. GCM large scale changes show a reduction in wind shear in addition to the expected strong increase in sea-surface temperature. Tropical cyclones from the 4 km historical simulation have a negative bias in intensity, not exceeding Category 4, and a wet bias in the rainfall associated with these cyclones. However, there is a clear improvement in cyclone intensity and rainfall at 4 km in comparison with the 12 km simulation. Climate change responses in the 4 km simulation include an extension of the tropical cyclone season, and strong increases in frequency of the most intense cyclones (approximately by a factor of 10) and associated rainfall. These are consistent with the results from the 12 km simulation.

Marine regulations aim to reduce sulfur and nitrogen exhaust emissions from maritime shipping. Here, two compliance pathways for reducing sulfur dioxide emissions, fuel sulfur content reduction and exhaust wet scrubbing, are studied for their effects on physicochemical properties and cloud forming abilities of engine exhaust particles. A test-bed diesel engine was utilized to study fresh exhaust emissions from combustion of non-compliant, high sulfur content fuel with (WS) and without (HiS) the usage of a wet scrubber as well as a regulatory compliant, low sulfur content fuel (LoS). Particle number emissions are decreased by ≈99% when switching to LoS due to absence of 20–30 nm sulfate rich particles. While number emissions for WS are also decreased, a shift in the sulfate mode toward larger sizes was found to increase particle mass emission factors by at least 31%. Changes in the mixing state induced by the compliance measures are reflected in the hygroscopicity of the exhaust particles. Fuel sulfur reduction decreased cloud condensation nuclei emissions by at least 97% due to emissions of primarily hydrophobic soot particles. Wet scrubbing increased those emissions, mainly driven by changes in particle size distributions. Our results indicate that both compliance alternatives have no obvious impact on the ice forming abilities of 200 nm exhaust particles. These detailed results are relevant for atmospheric processes and might be useful input parameters for cloud-resolving models to investigate ship aerosol-cloud interactions and to quantify the impact of shipping on radiative budgets from local to global scales.

Improved modeling of permafrost active layer freeze-thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze-thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non-gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications.

No abstract is available for this article.

This study explores the impact of sea surface temperature (SST) spatial heterogeneity on tropical cyclone (TC) intensity through a combination of observations and simulations, aiming to provide a reference for further improving TC intensity forecasting skills. Two distinct patterns of SST spatial heterogeneity are identified based on a statistical analysis of observational data, when the SST at the TC center is above and below 29.3°C, respectively. One is a warm-core pattern (WCP) with a warm peak SST at the TC center decreasing centrifugally which favors TC development, and the other one is a poleward-decreasing pattern (PDP) with a warm SST at the south decreasing poleward which suppresses TC development. The numerical simulations confirm the opposite influence of the WCP and the PDP on TC intensity. The WCP intensifies TC intensity by strengthening TC secondary circulation, increasing the conversion from ocean heat energy to TC kinetic energy, and compacting TC structure. In contrast, the PDP weakens TC intensity by inducing opposing responses of these processes. The magnitude of TC intensity change caused by SST spatial heterogeneity is comparable to those caused by a 1°C change in SST at the TC center. These findings offer valuable insights into the role of SST spatial heterogeneity in TC development and provide a new perspective to improve TC intensity forecasting by incorporating SST spatial heterogeneity into statistical-dynamic models.

Elves are observed as expanding rings of light in the UV and visible optical bands. They are produced when electromagnetic pulses from lightning discharges interact with the lower parts of the ionosphere. Elves are well known to be associated with high peak current lightning discharges. Here, we use data from the Modular Multi-spectral Imaging Array (MMIA) of the Atmosphere-Space Interactions Monitor (ASIM), and search for observations of Elves when high peak current lightning discharges are detected by Vaisala's Global Lightning Detection network GLD360. We present two groups of events; high peak current detections associated with Elves and high peak current detections not associated with Elves. To understand why some current pulses with high peak currents do not produce observable Elves, we investigate and compare the lightning activity occurring before these two types of events, in terms of both the number of lightning discharges detected by GLD360 and the peak currents of the preceding discharges. Our results, using data from GLD360, suggest that current pulses with peak currents above |120| kA tend to produce Elves nearly always, regardless of the preceding lightning activity. For current pulses with peak currents between |70| and |120| kA, the number of observed Elves might be affected by the preceding lightning activity, or is the result of the characteristics of the storm cells that produce the Elve.

As one of the important ecological responses of ecosystem to global climate change, forest expansion can alter land surface energy budget and local microclimate. Land surface temperature (LST) and soil temperature (ST) indicate the above- and below-ground thermal state, respectively. However, the lack of studies on the relationships between LST and ST changes after forest expansion hinders our understanding of the vegetation-microclimate interactions, especially during the dormant season. We quantified the change of LST (∆LST) and ST (∆ST) after forest expansion in the dormant season on the Qinghai-Tibet Plateau (QTP), and then explored their differences and linkages. The results showed that forest expansion significantly decreased LST by 0.75 ± 0.20°C, while significantly increased ST in three layers (0–7, 7–28, and 28–100 cm) by 0.31 ± 0.06, 0.29 ± 0.05, and 0.20 ± 0.04°C, respectively. The decreased LST was conducive to the preservation of snow, leading to larger snow depth, larger fractional snow cover and more snow cover days in forests than grasslands, which further promoted the increased ST through enhanced thermal insulation. Furthermore, it is suggested that the cooling impact of forest expansion on LST and warming impact on ST would both be stronger at humid sites than at dry sites. These findings will contribute to understand the vegetation-microclimate-snow interactions and the decoupling phenomenon between LST and ST in the dormant season influenced by vegetation change.

To quantify the volatility of organic aerosols (OA), a comprehensive campaign was conducted in the Chinese megacity. Volatility distributions of OA and particle-phase organic nitrate (pON) were estimated based on five methods: (a) empirical method and (b) kinetic model based on the measurement of a thermodenuder (TD) coupled with an aerosol mass spectrometer; (c) Formula-based SIMPOL model-driven method; (d) Element-based estimations using molecular formula measurements of OA; and (e) gas/particle partitioning. Our results demonstrate that the ambient OA volatility distribution shows good agreement between the two heating methods and the formula-based method when assuming ambient OA was mainly composed of organic nitrate (pON), organic sulfate and acid groups using the SIMPOL model. However, the element-based method tends to overestimate the volatility of OA compared to the above three methods, suggesting large uncertainties in the parameterizations or in the representativeness of the molecular measurements that need further refinement. The volatility of ambient OA is generally lower than that of the laboratory-derived secondary OA, emphasizing the impact of aging. A large fraction at the higher and lower volatility ranges (approximately log C* ≤ −9 and ≥2 μg m−3) was found for pON, implying the importance of both extremely low volatile and semi-volatile species. Overall, this study evaluates different methods for volatility estimation and gives new insight into the volatility of OA and pON in urban areas.

Chemistry Climate Models (CCMs) are essential tools for characterizing and predicting the role of atmospheric composition and chemistry in Earth's climate system. This study demonstrates the use of airborne in situ observations to diagnose the representation of chemical composition and transport by CCMs. Process-based diagnostics using dynamical and chemical coordinates are presented which minimize the spatial and temporal sampling differences between airborne in situ measurements and CCM grid points. The chosen process is the chemical impact of the Asian summer monsoon (ASM), where deep convection serves as a rapid transport pathway for surface emissions to reach the upper troposphere and lower stratosphere (UTLS). We examine two CCM configurations for their representation of the ASM UTLS using a set of airborne observations from south Asia. The diagnostics reveal good model performance at representing tropospheric tracer distribution throughout the troposphere and lower stratosphere, and excellent representation of chemical aging in the lower stratosphere when chemical loss is dominated by photolysis. Identified model limitations include the use of zonally averaged mole fraction boundary conditions for species with sufficiently short tropospheric lifetimes, which may obscure enhanced regional emissions sources. Overall, the diagnostics underscore the skill of current-generation models at representing pollution transport from the boundary layer to the stratosphere via the ASM mechanism, and demonstrate the strength of airborne in situ observations toward characterizing this representation.

Rugged topography considerably regulates the surface downwelling long-wave radiation (SDLR) flux and further affects the surface radiation and energy balances. The three dimensional sub-grid terrain long-wave radiative effect (3DSTLRE) is absent in most current numerical models, which usually adopt plane-parallel schemes to simulate the SDLR flux. This study has developed a clear-sky 3DSTLRE parameterization scheme based on the isotropic assumption of SDLR at rugged terrains and systematically evaluated its ability over the Tibetan Plateau (TP). Results show that the 3DSTLRE scheme achieves good and stable performance regardless of the horizontal resolution, time of the year, and sub-grid terrain complexity. At different model horizontal resolutions ranging from 0.025° to 0.8°, the normalized mean absolute errors (NMAE) of the daily SDLR flux simulated by the clear-sky 3DSTLRE scheme over most of TP are less than 0.9%, and the NMAE of the daily SDLR flux produced by the clear-sky 3DSTLRE scheme regionally averaged over the grids with different sub-grid terrain complexity are less than 0.25% in different months. Neglecting the 3DSTLRE in the plane-parallel schemes may lead to clearly underestimated SDLR flux over the rugged areas, and the underestimation increases with the horizontal resolution and sub-grid terrain complexity. At different model horizontal resolutions, the mean underestimation of the clear-sky daily SDLR flux simulated by the plane-parallel scheme over most of TP ranges from 5 to 20 W · m−2 with a relative underestimation of 4∼10%. The 3DSTLRE scheme can clearly reduce the biases of plane-parallel scheme and exhibits wide application prospects in various numerical models.

Ozone concentrations in China are increasing in recent years and future changes of ozone and their impacts have attracted much attention. We use global chemical transport model (GEOS-Chem) to simulate the surface ozone concentrations in China in 2020 and 2050 under four Shared Socio-economic Pathways and evaluate the impacts of future ozone pollution on vegetation and premature mortality in four polluted regions (Beijing–Tianjin–Hebei, BTH; Yangtze River Delta, YRD; Pearl River Delta, PRD; Sichuan Basin, SCB) and three major crop growing areas (Huang–Huai–Hai, HHH; Northeast Plain, NEP; middle and lower reaches of Yangtze River, MLRY) in China. The changes of simulated seasonal maximum daily 8-hr average (MDA8) ozone from 2020 to 2050 (−15.5 to +11.9 ppbv) are significant under SSP126 (low forcing pathway) and SSP245 (medium forcing pathway) scenarios in all regions due to large changes of emissions. MDA8 ozone in summer 2050 will be above the WHO guidelines (100 μg/m3) in BTH, YRD, HHH and MLRY under four scenarios. By 2050, W126 (vegetative ozone exposure index) in summer will be much above the maximum of US secondary standard (21 ppm-h) in HHH under SSP245, SSP370 (medium to high forcing pathway) and SSP585 (high forcing pathway) scenarios, and in MLRY under SSP370 and SSP585 scenarios. Annual ozone-related deaths for people over 30 years old will mainly decrease in four polluted areas from 2020 to 2050 under SSPs scenarios, but only increase much under SSP245 scenario in BTH (+3.1 to +4.2 thousand) and YRD (+1.1 to +1.6 thousand).

The mesosphere and lower thermosphere (MLT) plays a critical role in linking the middle and upper atmosphere. However, many General Circulation Models do not model the MLT and those that do remain poorly constrained. We use long-term meteor radar observations (2005–2021) from Rothera (67°S, 68°W) on the Antarctic Peninsula to evaluate the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X) and investigate interannual variability. We find some significant differences between WACCM-X and observations. In particular, at upper heights, observations reveal eastwards wintertime (April–September) winds, whereas the model predicts westwards winds. In summer (October–March), the observed winds are northwards but predictions are southwards. Both the model and observations reveal significant interannual variability. We characterize the trend and the correlation between the winds and key phenomena: (a) the 11-year solar cycle, (b) El Niño Southern Oscillation, (c) Quasi-Biennial Oscillation and (d) Southern Annular Mode using a linear regression method. Observations of the zonal wind show significant changes with time. The summertime westwards wind near 80 km is weakening by up to 4–5 ms−1 per decade, whilst the eastward wintertime winds around 85–95 km are strengthening at by around 7 ms−1 per decade. We find that at some times of year there are significant correlations between the phenomena and the observed/modeled winds. The significance of this work lies in quantifying the biases in a leading General Circulation Model and demonstrating notable interannual variability in both modeled and observed winds.