Journal of Geophysical Research: Atmospheres最新文献

The shortwave cloud radiative effect (SWCRE) is important for the Arctic surface radiation budget and is a major source of inter-model spread in simulating Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, we extend the existing Approximate Partial Radiative Perturbation (APRP) method by adding the absorptivity for the upward beam, considering differences in reflectivity between upward and downward beams, and analyzing the cloud masking effect resulting from changes in surface albedo. Using data from CMIP model experiments, the study decomposes the SWCRE over the Arctic surface and analyzes inter-model differences in quadrupled CO₂ simulations. The study accounts for the influence of surface albedo, cloud amount, and cloud microphysics in the response of SWCRE to Arctic warming. In the sunlit season, CMIP models exhibit a strong, negative SWCRE with a large inter-model spread. Arctic clouds dampen the surface albedo feedback by reflecting incoming solar radiation and further decrease the shortwave radiation reflected by surface, a fraction of which is scattered back to the surface by clouds. Specifically, this accounts for the majority of the inter-model spread in SWCRE. In addition, increased (decreased) cloud amount and cloud liquid water reduce (increase) incoming shortwave fluxes at the surface, but they are found to be not critical to the Arctic surface radiation budget and its inter-model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative processes, accurately decomposes the individual SWCRE responses at the Arctic surface.

Antarctic warm extremes impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyze temperatures exceeding the 90th percentile and the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the western periphery of high-pressure systems, consistent with anticyclonic advection. Temperature anomalies during warm extremes are strongest in winter due to the transport of warm and moist air and a strong meridional temperature gradient. In summer, the weak meridional gradients of top-of-atmosphere downward solar radiation flux and surface air temperature contribute to weak temperature anomalies. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.

We investigate the absolute momentum flux (AMF) and vertical wind variance $��\left(�\rho �\overline{w^{�\prime �2�}}�\right)�$ of gravity waves (GWs) along with intermittencies in the upper troposphere and lower stratosphere (UTLS) during 2017–2022 using the Middle Atmosphere Alomar Radar System at Andøya, Norway (69.30°N, 16.04°E). We categorized the AMF and $��\rho ��\overline{w^{�\prime �2�}}��$ into different period ranges (30 min–2 hr, 2–6 hr, 6–13 hr, 13 hr–1 day, and 30 min–1 day) to study the significance of short- and long-period waves. The selection of these period bands was based on the boundary conditions of the available spectra: 30 min (Nyquist frequency), 13 hr (inertial period), and 1 day (based on our interest in maximum long-period oscillations). Through the investigation of the AMF and $��\rho ��\overline{w^{�\prime �2�}}��$, we wish to determine in detail the GW characteristics at northern polar latitudes. Furthermore, it is crucial to assess the intermittency as

To better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TS_US) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TS_US predictions. For predicting TS_US, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TS_US during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TS_US, suggesting that the CNN models are able to capture the ENSO-TS_US teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TS_US is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TS_US forecast skill from subseasonal to seasonal timescales.

The Mediterranean region is experiencing pronounced aridification and in certain areas higher occurrence of intense precipitation. In this work, we analyze the evolution of the precipitation probability distribution in terms of precipitating days (or “wet-days”) and all-days quantile trends, in Europe and the Mediterranean, using the ERA5 reanalysis. Looking at the form of wet-days quantile trends curves, we identify four regimes. Two are predominant: in most of northern Europe the precipitation quantiles all intensify, while in the Mediterranean the low-medium quantiles are mostly decreasing as extremes intensify or decrease. The wet-days distribution is then modeled by a Weibull law with two parameters, whose changes capture the four regimes. Assessing the significance of the parameters' changes over 1950–2020 shows that a signal on wet-days distribution has already emerged in northern Europe (where the distribution shifts to more intense precipitation), but not yet in the Mediterranean, where the natural variability is stronger. We extend the results by describing the all-days distribution change as the wet-days’ change plus a contribution from the dry-days frequency change, and study their relative contribution. In northern Europe, the wet-days distribution change is the dominant driver, and the contribution of dry-days frequency change can be neglected for wet-days percentiles above about 50%. In the Mediterranean, however, the change of precipitation distribution comes from the significant increase of dry-days frequency instead of an intensity change during wet-days. Therefore, in the Mediterranean the increase of dry-days frequency is crucial for all-days trends, even for heavy precipitation.

It was recently suggested that the Sun could switch to a high-activity regime which would lead to a rise of ultraviolet radiation with an amplitude of about four times larger than the amplitude of an average solar activity cycle and a simultaneous drop in total solar irradiance. Here, we applied the SOCOLv3-MPIOM model with an interacting ocean to simulate the response of chemistry, dynamics, and temperature of Earth's atmosphere to such a change in solar irradiance. We studied the effect of high activity regime on the atmosphere investigating the influence of the chemical and radiative processes on the climate, and chemistry of NO_x, HO_x, and O₃. We find a climate cooling by up to 1K and a substantial increase in stratospheric ozone (up to 14%) and total ozone (up to 8%). To understand the role of the different processes we performed simulations with two sets of forcing accounting separately for the influence on chemical processes and for direct radiation energy balance. Our calculations show that the stratospheric O₃ response is almost fully driven by the chemical processes. We also found that the direct radiation processes lead to near-surface cooling that results in the suppression of the Brewer-Dobson circulation. This, in turn, leads to the reduction of H₂O influx from the low layers of the troposphere and to less intensive transport of ozone from the tropics to the middle latitudes. The surface climate response is dominated by direct radiation influence with only a small contribution from chemical processes.

In this study, the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.1 tropospheric air temperature, specific humidity, and relative humidity data are utilized to evaluate the global tropospheric temperature and humidity simulations in the fully coupled global climate models from the Coupled Model Intercomparison Project phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6), and possible simulation improvement in CMIP6 models in comparison to CMIP3 and CMIP5 models. Our analyses indicate that all three phases of CMIP models share similar tropospheric air temperature, specific humidity, and relative humidity biases in their multi-model ensemble means relative to AIRS. Cold biases up to 4 K and positive relative humidity biases up to 20% are found in the free troposphere almost globally with maxima over the mid-latitude storm tracks. Warm biases up to 2 K are seen over the Southern Ocean in the lower troposphere. Positive specific and relative humidity biases exist over the off-equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere, which are related to the double-intertropical convergence zone bias in the models. Both the air temperature and specific humidity biases are important to the relative humidity biases except in the tropical free troposphere where the specific humidity biases dominate. The tropospheric air temperature, specific humidity, and relative humidity biases are reduced from CMIP3 to CMIP5 and to CMIP6 at almost all pressure levels except at 300 hPa for specific humidity and in the boundary layer for relative humidity.

Surface solar radiation is fundamental for terrestrial life. It provides warmth to make our planet habitable, drives atmospheric circulation, the hydrological cycle and photosynthesis. Europe has experienced an increase in surface solar radiation, termed “brightening,” since the 1980s. This study investigates the causative factors behind this brightening. A novel algorithm from the EUMETSAT satellite application facility on climate monitoring (CM SAF) provides the unique opportunity to simulate surface solar radiation under various atmospheric conditions for clouds (clear-sky or all-sky), aerosol optical depth (time-varying or climatological averages) and water vapor content (with or without its direct influence on surface solar radiation). Through a multiple linear regression approach, the study attributes brightening trends to changes in these atmospheric parameters. Analyzing 61 locations distributed across Europe from 1983 to 2020, aerosols emerge as key driver during 1983–2002, with Southern Europe and high elevations showing subdued effects (0%/decade–1%/decade) versus more pronounced impacts in Northern and Eastern Europe (2%/decade–6%/decade). Cloud effects exhibit spatial variability, inducing a negative effect on surface solar radiation (−3%/decade–−2%/decade) at most investigated locations in the same period. In the period 2001–2020, aerosol effects are much smaller, while cloud effects dominate the observed brightening (2%/decade–5%/decade). This study therefore finds a substantial decrease in the cloud radiative effect over Europe in the first two decades of the 21st century. Water vapor exerts negligible influence in both sub-periods.

An independent evaluation of methane emissions data from GHGSat, a private company that operates a constellation of small microsatellites flying Fabry-Perot spectrometers operating at 1.6 µm, was performed. Data from multiple GHGSat commercial satellites, consisting of retrieved methane, diagnostics, and, where detected, plume and emissions information from roughly 250 scenes across Canada were analyzed. From these, 10 scenes contained methane plumes with a 2% detection rate for oil and gas scenes, and 10% for landfills. Methane precision was found to be 5%/2% on average for the C1/C2–C5 designs, with some variability due to scene albedo, terrain roughness, and airmass. Synthetic GHGSat plumes, generated using Lagrangian plume dispersion model and GHGSat characteristics, indicates typical detection limits of 240/180 kg/hr(C1/C2–C5), with a best case of roughly 100 kg/hr. Emissions and their uncertainties calculated using an alternative approach were in broad agreement with GHGSat-reported emissions. Overall, the performance of the GHGSat C2 design (also used for C3 onward) for favorable-viewing conditions was found to be largely consistent with company-advertised performance.

The detection efficiency (DE) of the World Wide Lightning Location Network (WWLLN) is evaluated in Southeast Asia by comparing WWLLN data with the Earth Network Total Lightning Network (ENTLN) data taking into account time, distance, and peak-current parameters. The performance of WWLLN is evaluated in the months of November and December in two different years (2020–2021). These periods are selected to assess the change (if any) in DE overtime and the inclusion of new stations. The strokes between the two networks were considered matched if they fell within a time criterion of 100 µs and a location difference of 25 km. Using this criterion, 5.2 × 10⁶ WWLLN strokes were matched with ENTLN cloud-to-ground (CG) lightning strokes in November-December 2020, resulting in a DE of 32.9%. Similarly, 4.6 × 10⁶ WWLLN strokes were found to match in November-December 2021, yielding a DE of 36.5%. Analysis of the peak-currents reveals that DE is lowest (<10%) for a peak-current below ±10 kA. However, for peak-current exceeding ±50 kA, the DE increases to ∼60%. During November-December 2021, WWLLN reported 38.95 × 10⁶ lightning strokes globally; amongst them, Dhaka station detected 0.5 × 10⁶ strokes, contributing to a 1.3% increase in the global DE. Dhaka station detects most lightning strokes within 8 × 10³ km, which diminishes to zero after 10 × 10³ km. The Dhaka station recorded a larger number of strokes at longer detection distances during midnight (22:00–02:00) than during noon (10:00–14:00). The results signify a positive impact of the Dhaka station on WWLLN's DE during the mentioned period.