Soil moisture influences near-surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture-temperature (SM-T) relationship is not spatially uniform, and numerous methods have been developed to assess SM-T coupling strength across the globe. These methods tend to involve either idealized climate-model experiments or linear statistical methods which cannot fully capture nonlinear SM-T coupling. In this study, we propose a nonlinear machine learning-based approach for analyzing SM-T coupling and apply this method to various mid-latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near-surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN’s TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM-T relationships broadly agree with previous assessments of SM-T coupling strength. Over many regions, we find nonlinear relationships between the CNN’s TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM-T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM-T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM-T coupling, our machine learning-based method can be extended to investigate other coupled interactions within the climate system using observed or model-derived datasets.

In 2021, the World Health Organization (WHO) ranked Nigeria among the most polluted nations in the world, an indication of a deteriorating air quality, especially in the major urban areas of the country, which might pose adverse human health impacts. In this study, the Integrated Source Apportionment Method (ISAM) tool in the Community Multiscale Air Quality (CMAQ) model (CMAQ-ISAM) was employed to quantify the contributions of eight emissions sectors to fine particulate matter (PM2.5) and its major components in Lagos during a prolonged severe atmospheric pollution episode (APE) in January 2021. The influence of meteorological conditions on the formation and dispersion of PM2.5 during the APE was also elucidated. Spatially, elevated PM2.5 concentrations were found in the northwestern region of Lagos, an urban area with larger anthropogenic emissions. Residential and industry were the two major sources of PM2.5. Residential contributed the most to total PM2.5 (~40 μg/m3), followed by industry (~20 μg/m3). High concentrations of secondary inorganic aerosols (SIA) at the northwest and upper northern areas of Lagos were majorly attributed to residential and industry sectors. In addition, sulfate accounted for the largest fraction of PM2.5, with residential, industry, and energy being its major sources. Residential, industry, and on-road sectors dominated the contributions to nitrate, while residential and industry were the major contributors to ammonium. Furthermore, the elevated PM2.5 concentrations during the APE were greatly enhanced by unfavorable meteorological conditions. This study provides insights for designing effective emissions control strategies to mitigate future severe PM2.5 pollution episode in Lagos.

The Arctic is marked by deep intrusions of warm, moist air, alternating with outbreaks of cold air down to lower latitudes. The typical vertical structure of clouds and precipitation during these two synoptic weather extremes is examined at a coastal site at 69°N in Norway. The Norwegian Sea is a corridor for warm-air intrusions (WAIs) and frequently witnesses cold-air outbreaks (CAOs). This study uses data from profiling radar, lidar, and microwave radiometer, radiosondes and other probes that were collected during the Cold air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) between 1 December 2019 and 31 May 2020. Marine CAOs are defined in terms of thermal instability relative to the sea surface temperature, and warm-air intrusions in terms of stratification of moist static energy between the surface and 850 hPa. Cloud structures in CAOs are convective, driven by strong surface heat fluxes over a long fetch of open water, with cloud tops between 2-4 km. The mostly open-cellular convection may contain substantial ice and produce intermittent moderate precipitation at the observational site, notwithstanding the low precipitable water vapor. In contrast, WAIs are marked by high values of precipitable water vapor and integrated vapor transport. WAI clouds are stratiform, with cloud tops often exceeding 6 km, sometimes layered, and generally producing persistent precipitation that can be heavier than in CAOs.

Attention is increasingly being turned towards an investigation of extreme hydrometeorological events within the context of land-atmosphere coupling in the wider hydrological cycle, particularly with respect to the identification of compound and seesaw events. To examine these events, accurate soil moisture data are essential. Here, soil moisture from three reanalysis products (ERA5-Land, BARRA and ERA5) are compared to station observations from 12 sites across New Zealand for an average timespan of 18 years. Soil moisture data from all three reanalyses were subsequently used to investigate land-atmosphere coupling with gridded (observational) precipitation and temperature. Finally, compound (co-occurrence of hot and dry) and seesaw (transitions from dry to wet) periods were identified and examined. No best performing reanalysis dataset for soil moisture is evident (min r = 0.78, max r = 0.80). All datasets successfully capture the seasonal and residual component of soil moisture, but not the observed soil moisture trends at each location. Strong coupling between soil moisture and temperature occurs across the predominately energy-limited regions of the lower North Island and entire South Island. Consequently, these regions reveal a high frequency of compound period occurrence and potential shifts in land states to a water limited phase during compound months. A series of seesaw events are also detected for the first time in New Zealand (terminating an average of 17% of droughts), with particularly high frequency of seesaw event occurrence detected in previously identified areas of atmospheric river (AR) activity, indicating the likely wider significance of ARs for drought termination.

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 (i) the occurrence of RWB events after the propagation of RWPs, (ii) 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 (iii) the proportion of blocking situations that occur near RWB events. To do so, we applied a tracking algorithm to detect RWPs in the Southern Hemisphere during summertime between 1979-2020, 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 propagating 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 the Southern Annular Mode and El Niño-Southern Oscillation.

This study derives radiatively-active hydrometeors frequencies (HFs) from CloudSat-CALIPSO satellite data to evaluate cloud fraction in present-day simulations by CMIP5 models. Most CMIP5 models do not consider precipitating and/or convective hydrometeors but CESM1-CAM5 in CMIP5 has diagnostic snow and CESM2-CAM6 in CMIP6 has prognostic precipitating ice (snow) included. However, the models do not have snow fraction available for evaluation. Since the satellite-retrieved hydrometeors include the mixtures of floating, precipitating and convective ice and liquid particles, a filtering method is applied to produce estimates of cloud-only HF (or NPCHF) from the total radiatively-active HF (THF), which is the sum of NPCHF, precipitating ice HF and convective HF. The reference HF data for model evaluation include estimates of liquid-phase NPCHF from CloudSat radar-only data (2B-CWC) and ice-phase THF from CloudSat-CALIPSO 2C-ICE combined radar/lidar data. The model evaluation results show that cloud fraction from CMIP5 multi-model mean (MMM) is significantly underestimated (up to 30 %) against the total HF estimates, mainly below the mid-troposphere over the extratropics and in the upper-troposphere over the midlatitude lands and a few tropical convective regions. The CMIP5 cloud fraction biases are reduced dramatically when compared to the cloud-only HF estimates, but the area of overestimates expands from the tropical convective regions to mid-latitudes in the lower and upper troposphere. There is no CMIP5 standard output snow fraction available for comparison against CloudSat-CALIPSO estimate. The implications of these results show that hydrometeors frequency estimates from CloudSat-CALIPSO provide a reference for GCM’s cloud fraction from stratiform and convective form.

The strength of Pacific Walker circulation (PWC) significantly affects the global weather patterns, the distribution of mean precipitation, and modulates the rate of global warming. Different indices have been used to assess the PWC strength. Evaluated on different datasets for various study periods, the indices show large discrepancies between the reported trends. In this study, we performed sensitivity analysis of 10 PWC indices and compared them over the 1951-2020 period using the ERA5 reanalyses. The time series of normalised indices generally agree on the annual-mean PWC strength. The highest correlations (exceeding r=0.9) are between the indices that describe closely linked physical processes. The trends of PWC strength are strongly affected by the choice of representative time period. For the commonly used 1981-2010 period, the trends show strengthening of the PWC. However, trends computed for longer period (i.e. 1951-2020) are mostly neutral, whereas the past two decades (2000-2020) display weakening of the PWC, although it is statistically not significant. The temporal evolution of trends suggests multidecadal variability of PWC strength with a period of about 35 years, implying a continued weakening of the PWC in the next decade.

We studied atmospheric methane observations from November 2016 to October 2017 from one rural and two urban towers in the Baltimore-Washington region (BWR). Methane observations at these three towers display distinct seasonal and diurnal cycles with maxima at night and in the early morning, reflecting local emissions and boundary layer dynamics. Peaks in winter concentrations and vertical gradients indicate strong local anthropogenic wintertime methane sources in urban regions. In contrast, our analysis shows larger local emissions in summer at the rural site, suggesting a dominant influence of wetland emissions. We compared observed enhancements (mole fractions above the 5th percentile) to simulated methane enhancements using the WRF-STILT model driven by two EDGAR inventories. When run with EDGAR 5.0, the low bias of modeled versus measured methane was greater (ratio of 1.9) than the bias found when using the EDGAR 4.2 emission inventory (ratio of 1.3). However, the correlation of modeled versus measured methane was stronger (~1.2 times higher) for EDGAR 5.0 compared to results found using EDGAR 4.2. In winter, the inclusion of wetland emissions using WETCHARTs had little impact on the mean bias, but during summer, the low bias for all hours using EDGAR 5.0 improved by from 63 to 23 nanomoles per mole of dry air or parts per billion (ppb) at the rural site. We conclude that both versions of EDGAR underestimate the regional anthropogenic emissions of methane, but version 5.0 has a more accurate spatial representation.

Extending across seven countries, the Alps represent an important element for climate and atmospheric circulation in Central Europe. Its complex topography affects processes on different scales within the atmospheric system. This is of major relevance for the decadal trends in Surface Solar Radiation (SSR), also known as Global Dimming and Brightening (GDB). In this study we analysed data from 14 stations in and around the Swiss and Austrian Alps, over a period ranging from the 1960s up to the 2010s, with the aim of characterizing the spatio-temporal variations of the GDB and understanding the causes for such trends in this region. Our results showed a different behavior in the SSR decadal trends in the western part of the Alps in comparison to the eastern part. We also identified a remarkable difference between the causes of such trends in the stations at low altitudes in comparison to the station at higher altitudes. The SSR trends under cloudy conditions revealed strong evidence for a control of the decadal trends by cloud optical depth at high elevation sites, in contrast with a strong clear-sky forcing at low elevations. Results from previous literature and available data suggest that such phenomena could be associated with the indirect and direct aerosol effect, respectively, due to differing pollution levels.

This study evaluates high-latitude stratiform mixed-phase clouds (SMPC) in the atmosphere model of the newly released Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one-year-long ground-based remote sensing measurements from the U.S. Department of Energy Atmospheric Radiation and Measurement (ARM) Program. A nudging approach is applied to model simulations for a better comparison with the ARM observations. Observed and modeled SMPCs are collocated to evaluate their macro- and microphysical properties at the ARM North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates (underestimates) SMPC frequency of occurrence at the NSA (AWR) site nearly all year round. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud boundaries and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phases are substantially biased at each location. The model largely overestimates liquid water path at NSA, whereas it is frequently underestimated at AWR. Meanwhile, the simulated ice water path is underestimated at NSA, but at AWR, it is comparable to observations. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that continuous improvement in cloud microphysics is needed for high-latitude mixed-phase clouds.

The region around the tip of the Antarctic Peninsula is one of the fastest warming regions of the world, a situation that will lead to widespread changes in permafrost state, local hydrological cycles and biological activity. Further, it is located in the path of the southern westerly winds, one of the poorest-understood components of the global climatic system. The sedimentary archives in the lakes from the ice-free regions on this region host a yet untapped wealth of information on the past changes and links between the regional climatic, hydrologic and biological systems. Especially important are the stable isotope compositions of these sediments, but to understand how they record these changes, an in-depth knowledge of their links to present-day conditions is required. We present here the first study of the stable isotope composition of the surface waters in the ice-free southern peninsulas of King George Island, Antarctica. Our results suggest that a clear separation of the various water bodies (permafrost, snow, meltwater, lakes) based on the stable isotope composition of the water is possible, allowing for future studies aiming to understand (changing) feeding behavior of terrestrial fauna. Further, water in lakes on a W-E transect have distinct stable isotope composition, leading to the possibility of studying the past changes in the strength and dynamics of the westerly winds in the region.

Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document the magnitude and extent of minimum temperature biases in the CMIP6 model ensemble, relative to ERA5. Bias in the southern Cascadia region (i.e. Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains) stands out relative to the rest of North America, with some models showing a bias in excess of -10°C in the 1st percentile of daily winter minimum temperature. During the coldest minimum temperature days, the CMIP6 models show an anomalous high in mean sea level pressure in the Northeast Pacific – an atmospheric blocking pattern that is also present in ERA5. While this atmospheric blocking pattern is typically concurrent with cold temperatures across much of North America, terrain barriers such as the Rockies and Cascades prevent the cold air from reaching the Pacific Northwest in observation and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from the interior of the continent.

Information from total electron content (TEC) from Global Navigation Satellite Systems (GNSS) could be assessed to know the impact of weather events to help in developing prediction and warning systems. The majority of studies focus on the occurrence of only one event neglecting situations where these weather events happen almost simultaneously or consecutively. This current study tends to fill the gap by analyzing ionosphere response following the simultaneous and/or consecutive occurrence of geomagnetic storm and lightning events at various intensities in southern China coastal region. The results showed that the magnitude of the frequency lightning-related events using continuous wavelet transform (CWT) was 0.3-0.4 while that of geomagnetic storm was 0.15-0.3. However, the various levels of intensity could not be distinguished. Being able to differentiate the weather events by the magnitude values following the ionosphere response is good for prediction and modeling purposes as the use of TEC in some studies does not provide this clear distinction.