JGR–Atmospheres

Climate feedbacks over the historical period (here defined as 1850–2014) have been investigated in large ensembles of historical and single forcing experiments (hist-ghg, hist-aer, and hist-nat), with 47 members for each experiment. Across the historical ensemble with all forcings, a range in estimated Effective Climate Sensitivity (EffCS) between approximately 3–6 K is found, a considerable spread stemming solely from initial condition uncertainty. The spread in EffCS is associated with varying Sea Surface Temperature (SST) patterns seen across the ensemble due to their influence on different feedback processes. For example, the level of polar amplification is strongly correlated with the amount of sea ice melt per degree of global warming. This mechanism is related to the large spread in shortwave clear-sky feedbacks and is the main contributor to the different forcing efficacies seen across the different forcing agents, although in HadGEM3-GC3.1-LL these differences in forcing efficacy are shown to be small. The spread in other feedbacks is also investigated, with the level of tropical SST warming strongly correlated with the longwave clear-sky feedbacks, and the local surface-air-temperatures well correlated with the spread in cloud radiative effect feedbacks. The metrics used to understand the spread in feedbacks can also help to explain the disparity between feedbacks seen in the historical experiment simulations and modeled estimate of the feedbacks seen in the real world derived from an atmosphere-only experiment prescribed with observed SSTs (termed amip-piForcing).

Zonal extensions of the Western Pacific subtropical high (WPSH) strongly modulate extreme rainfall activity and tropical cyclone (TC) landfall over the Western North Pacific (WNP) region. These zonal extensions are primarily forced on seasonal timescales by inter-basin zonal sea surface temperature (SST) gradients. However, despite the presence of large-scale zonal SST gradients, the WPSH response to SSTs varies from year to year. In this study, we force the atmosphere-only NCAR Community Earth System Model version 2 simulations with two real-world SST patterns, both featuring the large-scale zonal SST gradient characteristic of decaying El Niño-developing La Niña summers. For each of these patterns, we performed four experimental sets that tested the relative contributions of the tropical Indian Ocean, Pacific, and Atlantic basin SSTs to simulated westward extensions over the WNP during June–August. Our results indicate that the subtle differences between the two SST anomaly patterns belie two different mechanisms forcing the WPSH's westward extensions. In one SST anomaly pattern, extratropical North Pacific SST forcing suppresses the tropical Pacific zonal SST gradient forcing, resulting in tropical Atlantic and Indian Ocean SSTs being the dominant driver. The second SST anomaly pattern drives a similar westward extension as the first pattern, but the underlying SST gradient driving the WPSH points to intra-basin forcing mechanisms originating in the Pacific. The results of this study have implications for understanding and predicting the impact of the WPSH's zonal variability on tropical cyclones and extreme rainfall over the WNP.

In this study, a variable-resolution version of the Community Earth System Model (VR-CESM) with mesh refinement (∼0.125°) over East Asia is used to simulate the regional climate in this region. For the evaluation of model performance and sensitivity to model resolution, the simulated near-surface temperature and precipitation are compared with observations and simulation results from a globally quasi-uniform (∼1°) CESM (UN-CESM). Results show that VR-CESM better simulates the spatial patterns and seasonal variations of mean temperature and precipitation than UN-CESM over China. For extreme events, VR-CESM improves the simulation of the occurrence frequency of wintertime daily minimum temperature and heavy precipitation. In regions with complex terrains, VR-CESM better resolves the topographic forcing and captures the observed fine-scale spatial patterns of temperature and precipitation, although precipitation is still overestimated. For East Asian summer monsoon precipitation, both UN-CESM and VR-CESM tend to overestimate (underestimate) the precipitation over northern (southern) China, which is associated with too strong meridional water vapor transport in the models and biases in the large-scale circulation in the middle and upper troposphere. Different from previous studies with different physics parameterizations and refined domains, as the model resolution increases, simulated monsoon precipitation evolution is not obviously improved, and convective precipitation intensity decreases over eastern China. Despite this, our results indicate that VR-CESM simulates regional climate, topographical forcing, and large-scale circulations over East Asia reasonably well, and thus it can be applied for the future climate projection in the region.

Based on multi-model large-ensemble experiments provided by Polar Amplification Model Intercomparison Project (PAMIP), we investigate the influence of the projected sea ice loss in Barents-Kara Seas (BKS) and Sea of Okhotsk (SOK) on the Arctic stratospheric polar vortex (SPV). Results show that future BKS sea ice reduction leads to a weakened SPV during November-February by enhancing the upward-propagating planetary wave 1, which is more pronounced during Quasi-Biennial Oscillation (QBO) easterly than westerly phase. Through weakening the upward-propagating planetary wave 2, future SOK sea ice reduction is favorable for a strengthened SPV during January-April. Inter-model spread in the magnitudes of SPV responses to BKS sea ice reduction can be largely explained by the divergent planetary wave responses, but less so for SOK sea ice reduction. Results from a linearized baroclinic model further validate the importance of the planetary-scale wave responses in explaining the differing SPV responses to sea-ice loss over the two regions.

This study presents the implementation and evaluation of scale-dependent localization (SDL) in the hurricane analysis and forecast system 4D Ensemble Variational (4DEnVar) Data Assimilation (DA) system. The SDL capability is compared with the traditional Single-Scale Localization (SSL) method to assess its benefits and necessity for hurricane prediction. The experiments focus on Hurricane Laura (2020) and involve single observation experiments as well as real observation DA cycling experiments. The results indicate that the SDL experiment, which incorporates the Fast Almost Gaussian Filtering approach for scale decomposition, consistently outperforms the corresponding SSL configurations in almost all aspects. Further diagnostics show that due to its multiscale nature, the SDL approach demonstrates better track prediction over small-scale SSL due to improved environmental analysis and better analyzed vortex position and structure, and superior intensity prediction during the rapid intensification over both the large-scale SSL and the small-scale SSL owing to enhanced inner-core thermodynamic analysis.

Overshooting convection can significantly impact the chemical and radiative properties of the upper troposphere and lower stratosphere through the transport of various chemical species. These impacts include enhancements of water vapor and ozone-depleting halocarbons, which both have important consequences for climate change. Therefore, accurate prediction of the Earth's climate system requires convective overshooting to be included. To better understand how convective transport is represented in current state-of-the-art models, approximately 75,000 individual updrafts in the central and eastern United States are analyzed from High-Resolution Rapid Refresh (HRRR) simulations and NEXRAD radar observations during May and July 2021. Distributions of echo top potential temperatures and heights, as well as diurnal cycles of overshooting frequency, are compared to observations. These distributions show mean, median, and maximum echo tops 2–3 km lower than observations, both in absolute and tropopause-relative space, with evidence of updrafts losing momentum too rapidly above the tropopause. Diurnal cycles show accurate times of maximum and minimum overshooting, but significant errors at model initialization and evidence that some simulated overshoots continue too late into the overnight hours. Despite these deficiencies, distributions of simulated levels of maximum detrainment show decent agreement with observations. All results, including the severe underprediction of echo top heights, persist at shorter forecast lead times. This indicates a need to improve representation of overshooting storms in weather and climate models, even those that are convection-permitting, or introduce a transport parameterization.

The climate characteristics of remote rainfall events in Taiwan from September to February over 41 years (1980–2020) are studied. These events are induced by the interaction between the northeasterly flow and the typhoon's outer circulation. Our findings reveal that rainfall in northeastern Taiwan becomes more prominent when tropical cyclones move to the remote rainfall-prone area, located in the north Philippine area to the northern South China Sea, and when the background northeasterly wind speed exceeds 7 m s−1. Under these criteria, the confluence of the typhoon's outer circulation and the northeasterly flow creates a convergence area that enhances rainfall in northeastern Taiwan, increasing the occurrence of moderate to extreme rainfall (ER) events. This leads to an average enhancement in rainfall amount of 80–220 mm per day. Additionally, when typhoons are in the remote rainfall-prone area, there is a greater than 20% chance for events with maximum rainfall over 200 mm day−1 to occur, particularly in the region of 20°–22°N, 116°−124°E, and north to Luzon Island. In this area, the occurrence rate can exceed more than a 45% chance. The highest risk of ER events occurs between 20°–22°N and 118°−120°E, with a probability of over 90%. Notably, the convergence area for the Taiwan cases does not necessarily coincide with the baroclinic forcing as that associated with remote rainfall events observed in Japan, Korea, and North America.

No abstract is available for this article.

To understand future summer precipitation changes over the Great Lakes Region (GLR), we performed an ensemble of regional climate simulations through the Pseudo-Global Warming (PGW) approach. We found that different types of convective precipitation respond differently to the PGW signal. Isolated deep convection (IDC), usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), usually concentrated upwind of the GLR, shift to the downwind region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to increase across almost the entire studied domain, creating a potential environment more favorable for stronger convection systems and less favorable for weaker ones. Meanwhile, changes in the lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, highlighting the significance of these thermodynamic factors in controlling precipitation over the domain. Our results indicate that the decrease in LCL and LCF in areas with increased convective precipitation is mainly due to increased atmospheric moisture. In response to the prescribed warming perturbation, MCSs occur more frequently downwind, while localized IDCs exhibit more intense rain rates, longer durations, and larger rainfall area.

Improving tropical cyclone (TC) rainfall prediction is vital as climate change has led to an increase in TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to make use of them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. While the Fuzzy C Means (FCM) clustering algorithm is widely used, it has limitations stemming from its clustering-centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn distance, a novel similarity metric that measures the cost of transforming one set of data to another, for assessing TC similarity in rainfall prediction. Our findings indicate that utilizing Sinkhorn distance enhances the accuracy of TC rainfall predictions across the Western North Pacific region. When compared to the conventional approach using FCM, our Sinkhorn distance-based methodology yields slightly better yet statistically significant results. The improvement is due to better identification of similar TCs, characterized by closer proximity of similar TC tracks to the target TC track, facilitated by Sinkhorn distance. This underscores how minor differences in TC track can alter rainfall distribution, emphasizing the critical importance of accurate track prediction in rainfall prediction and the need to reconsider how we categorize TCs together, which can have implications for climate and atmospheric sciences. Collectively, the inclusion of Sinkhorn distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the accuracy of TC rainfall predictions.

Convective dynamics in a supercell thunderstorm, a volcanic eruption, and two pyrocumulonimbus (pyroCb) events are compared by computing cloud-top divergence (CTD) with an optical flow technique called Deepflow. Visible 0.64-μm imagery sequences from Geostationary Operational Environmental Satellites (GOES)-R series Advanced Baseline Imager (ABI) are used as input into the optical flow algorithm. CTD is computed after post-processing of the retrieved motions. Analysis is performed on specific image times, as well as the full time series of each case. Multiple CTD-based parameters, such as the maximum and the two-dimensional area exceeding a specified CTD threshold, are examined along with the optical flow-retrieved wind speed. CTD is shown to accurately and quantitatively represent the behavior and magnitude of different deep convective phenomena, including distinguishing between convective pulses within each individual event. CTD captures updraft intensification as well as differences in convective activity between two pyroCb events and individual updraft pulses occurring within a single pyroCb event. Finally, the characteristics of high-altitude smoke plumes injected by two separate pyroCb pulses are linked to CTD using ultraviolet aerosol index and satellite imagery. Optical flow-derived parameters can therefore be applied to individual pyroCbs in real-time, with potential to characterize pyroCb smoke source inputs for downstream smoke modeling applications and to facilitate future tools supporting air quality modeling and firefighting efforts.

Iron (Fe) has profound impacts on Earth's ecosystem and global biogeochemical cycles. Fe deposited onto glacier surfaces reduces snow and ice albedo, thereby accelerating glacier melting, and supplying downstream ecosystems with dissolved Fe. However, the origins of atmospheric Fe deposition in glacier regions of western China remain unclear. This study presents novel insights into Fe isotopic composition (refer to δ56Fe) and origins, gained from geochemical analysis of large-scale cryoconite samples collected from glaciers in western China, which encompass the Tibetan Plateau (TP) and the Tianshan Mountains. Results showed that cryoconite δ56Fe ranged from −1.06 ± 0.07‰ to 0.33 ± 0.04‰, regardless of their concentration. Moreover, anomalous δ56Fe values deviating significantly from the upper continental crust values (with an average of 0.09‰) were detected, indicating a significant impact of anthropogenic Fe materials on the investigated glaciers. This impact was particularly prominent in the margin regions of the TP and its surroundings, but was less apparent in the interior and southern of the plateau. Using MixSIAR isotope mixing model, we determined that coal combustion and other anthropogenic combustion sources (such as liquid fuel combustion and steel smelting) contributed to cryoconite Fe in the range of 6.9%–43.1% and 0.8%–23.4%, respectively. Among these, coal combustion was the predominant anthropogenic source of cryoconite Fe in western China's glaciers. Compared with other sink areas in the Northern Hemisphere, glaciers in western China are obviously affected by anthropogenically sourced Fe. This study has significant implications for understanding glacier-fed downstream ecosystems and the regional biogeochemical cycle.

Sea-air methane flux was measured directly by the eddy-covariance method across approximately 60,000 km of Arctic and Antarctic cruises during a number of summers. The Arctic Ocean (north of 60°N, between 20°W and 50°E) and Southern Ocean (south of 50°S, between 70°W and 30°E) are found to be on-shelf sources of atmospheric methane with mean sea-air fluxes of 9.17 ± 2.91 (SEM (standard error of the mean)) μmol m−2 d−1 and 8.98 ± 0.91 μmol m−2 d−1, respectively. Off-shelf, this region of the Arctic Ocean is found to be a source of methane (mean flux of 2.39 ± 0.68 μmol m−2 d−1), while this region of the Southern Ocean is found to be a methane sink (mean flux of −0.77 ± 0.37 μmol m−2 d−1). The highest fluxes observed are found around west Svalbard, South Georgia, and South Shetland Islands and Bransfield Strait; areas with evidence of the presence of methane flares emanating from the seabed. Hence, this study may provide evidence of direct emission of seabed methane to the atmosphere in both the Arctic and Antarctic. Comparing with previous studies, the results of this study may indicate an increase in sea-air flux of methane in areas with seafloor seepage over timescales of several decades. As climate change exacerbates rising water temperatures, continued monitoring of methane release from polar oceans into the future is crucial.

Daily mean albedo, a crucial variable of the earth radiation budget, is significantly affected by the diurnal variation of land surface albedo (DVLSA). The DVLSA typically exhibits asymmetry, thereby affecting the estimation of the daily mean albedo. However, the asymmetry in the DVLSA is generally ignored in daily mean albedo estimation. In this study, we investigated the influencing factors of the asymmetry in the DVLSA and evaluated its impacts on estimating the daily mean albedo based on field observations and simulated data. Our findings reveal that the asymmetry in the DVLSA varies among land cover types, with forests exhibiting more pronounced asymmetry compared to croplands, grasslands, and bare soil. The diurnal variation of the atmospheric conditions is the primary factor controlling the asymmetry in the DVLSA, with that of land surface conditions being a secondary factor. Neglecting the asymmetry in the DVLSA leads to estimation error in daily mean albedo, particularly pronounced during winter. The relative error of daily mean albedo can exceed 10% when the mean asymmetry index of diffuse irradiance fraction reaches 40%. However, the DVLSA retrieved from the satellite Bidirectional Reflectance Distribution Function product inadequately captures asymmetry, resulting in a relative error of approximately 13.7% in estimating daily mean albedo.

The Spring Predictability Barrier (SPB) phenomenon is characterized by the reduced accuracy of El Niño/Southern Oscillation (ENSO) forecasts during the spring, which substantially limits our ability to predict ENSO events. By investigating the nonlinear dynamic characteristics of ENSO systems simulated by a box model, we found that the strong surface heating process in spring may contribute to the SPB by regulating the different coupling processes between the ocean and atmosphere. Specifically, the intensified springtime surface heating increases the Sea Surface Temperature (SST), further amplifying the thermal damping effect of SST anomalies and reducing the dynamic connection between zonal SST gradient and upwelling process, and finally increasing the chaotic degree of ENSO systems simulated by the box model. The enhanced chaotic degree of ENSO systems leads to a more rapid growth of initial errors in the forecast model in spring, potentially leading to the SPB phenomenon.

During marine cold-air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well-recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground-based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka-band radar are identified using a watershed segmentation method. Using self-organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM-based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation-induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non-precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

We calculate the climate forcing for the 2 ys after the 15 January 2022, Hunga Tonga-Hunga Ha'apai (Hunga) eruption. We use satellite observations of stratospheric aerosols, trace gases and temperatures to compute the tropopause radiative flux changes relative to climatology. Overall, the net downward radiative flux decreased compared to climatology. The Hunga stratospheric water vapor anomaly initially increases the downward infrared radiative flux, but this forcing diminishes as the anomaly disperses. The Hunga aerosols cause a solar flux reduction that dominates the net flux change over most of the 2 yrs period. Hunga induced temperature changes produce a decrease in downward long-wave flux. Hunga induced ozone reduction increases the short-wave downward flux creating small sub-tropical increase in total flux from mid-2022 to 2023. By the end of 2023, most of the Hunga induced radiative forcing changes have disappeared. There is some disagreement in the satellite measured stratospheric aerosol optical depth (SAOD) observations which we view as a measure of the uncertainty; however, the SAOD uncertainty does not alter our conclusion that, overall, aerosols dominate the radiative flux changes.

As the Arctic rapidly warms, sea ice extent is decreasing and oil and gas extraction activities are expanding. Local combustion emissions affect the Arctic atmospheric aerosol chemical mixing state (the distribution of chemical species across the aerosol population), which impacts climate-relevant properties. Bulk and single-particle measurements of submicron aerosols were conducted at Oliktok Point, Alaska within the North Slope of Alaska oil fields. In this work, we quantify aerosol diversity using online single-particle mass spectrometry data (32,880 individual particles), offline single-particle microscopy data (20,912 individual particles), and online bulk aerosol mass spectrometry and aethalometer data. This method was used to derive individual particle mass fractions for both refractory and non-refractory material within distinct particle types. Single-particle, average single-particle, and bulk population diversities (D i , D α , D γ , respectively) and mixing state indices (χ) were calculated for the data set. Calculated D i values were generally low (2.2 ± 0.6), as individual particle masses were dominated by a few chemical species of interest. Aged aerosol particles (those internally mixed with nitrate and/or sulfate) exhibited higher D i values (>3) compared to recently emitted (fresh) aerosol particles. During oil field plume periods, D α values approached three due to the abundance of diesel combustion particles, which were rich in sulfate, black carbon, and organic aerosol. Overall, the submicron aerosol population within the Arctic oil fields was found to be relatively externally mixed (χ < 50%), due to the constant local emissions within oil fields combining with background aerosol and locally emitted sea spray aerosol at the coastal site.

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modeling. Atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (UK) and Republic of Ireland are used to derive monthly N2O emissions for 2013–2022 using two different inverse methods. We find mean UK emissions of 90.5 ± 23.0 (1σ) and 111.7 ± 32.1 (1σ) Gg N2O yr−1 for 2013–2022, and corresponding trends of −0.68 ± 0.48 (1σ) Gg N2O yr−2 and −2.10 ± 0.72 (1σ) Gg N2O yr−2, respectively, for the two inverse methods. The UK National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 ± 1.7 (1σ) Gg N2O yr−1 across this period, which is 22%–51% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up UK Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top-down (atmospheric data-based) and bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.

Narrow bipolar events (NBEs) are impulsive and powerful intracloud discharges. Recent observations indicate that some NBEs exhibit a slanted orientation rather than strictly vertical. This paper investigates the effect of the slanted NBEs using a newly developed rebounding-wave model. The modeling results are validated against the full-wave Finite- Difference Time-Domain method and compared with measurements for both vertical and slanted NBE cases. It is found that the inclination of the NBEs affects both the waveforms and amplitudes of the electrostatic, induction and radiation components of the electric fields at close distances (≤10 km). However, it primarily influences the amplitudes of the fields for distances beyond 50 km, where the radiation component dominates, resulting in changes of ≥30% when the slant angle exceeds 30°. The slanted rebounding-wave model improves the agreement with respect to a purely vertical channel and can be extended to any discharge geometry at arbitrary observation distances.