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Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals. [ABSTRACT FROM AUTHOR]

[Objective] Understanding and grasping the temporal and spatial changes in meteorology and reasonably predicting the future trend of climate change can provide an important theoretical basis for the formulation of regional response measures. [Methods] The Sichuan section of the Yangtze River Basin was selected as the research area. Data analysis was conducted based on measured station data from 1980 to 2014. Three future hypothetical climate scenario models (SSP1-2.6, SSP2-4.5, SSP5-8.5) were estimated using the GCM with complete scenarios in the sixth phase of the International Coupled Model Comparison Plan (CMIP6) after bias correction, and the changes in precipitation climate variables within the region were analyzed. [Results (1) There is a certain degree of underestimation of precipitation simulation in climate models, with monthly scale seasonal scale annual scale in terms of time scale. (2) After the calibration and evaluation of the climate model, the data are more consistent with the actual situation, and the quantile mapping method is very effective for the calibration of the Climate model data. (3) Compared to the benchmark period (1980-2014), the future precipitation in the study area is higher than the historical period under three scenarios, with SSP5-8.5 SSP2-4.5 SSP1-2.6. (4) The areas with significant increases in precipitation are mainly located in the northwest of the study area, showing a spatial pattern of low level in the west and high level in the east. [Conclusion] The climate model's prediction of future precipitation changes in the study area shows an overall increasing trend, but there is underestimation to a certain extent. [ABSTRACT FROM AUTHOR]

Dust and high cloud interactions are critical for climate change, primarily due to the dominant roles of high cloud in the greenhouse effect and the continental precipitation. Nonetheless, disentangling the specific impacts of dust from the overlying meteorology influence on high clouds presents great challenges. In this study, we construct a meteorological pattern that successfully reveal the intricate connection between high cloud distribution and atmospheric conditions. Through this strong bounded relationship, we find that dust exhibits notable controls over the facilitation or inhibition of ice particle growth contingent upon the prevailing meteorological fields. More dust can increase precipitation rate and shorten high cloud lifetime particularly under favorable meteorology. These findings underscore the crucial role of dust concentration in mitigating global warming for future climate change. Plain Language Summary: Dust particles in the atmosphere play a crucial role in the formation and dissipation of high clouds, which can significantly impact global climate and weather patterns. By analyzing satellite observations and climate model data, we discovered that dust can either enhance or suppress the growth of ice crystals, depending on the atmospheric conditions. In moist environments with favorable weather, increased dust leads to larger ice crystals and more precipitation, accelerating the life cycle of high clouds. Conversely, in dry conditions with unfavorable weather, excessive dust competes for limited moisture, resulting in smaller ice crystals, although it can still increase rainfall during the dissipation stage of high clouds. As climate change progresses and dust storms become more frequent, the increased presence of dust in the atmosphere may lead to more extreme weather events and potentially mitigate some of the warming effects caused by high clouds. Understanding these complex interactions between dust, high clouds, and weather conditions is essential for improving climate models and predicting future changes in Earth's water cycle and energy balance. Key Points: Extracted first principal component, highly correlated with high cloud fraction, isolating meteorological effects on cloudsDust facilitates ice crystal formation and precipitation, catalyzing cloud lifecycle and ultimately modifying radiative effectsIncreased dust accelerates high cloud lifecycle, causing more extreme weather and mitigating climate warming [ABSTRACT FROM AUTHOR]

The Turkana Jet plays a pivotal role in the meteorology of East Africa across timescales, and owes its existence to both large‐scale dynamics and the representation of intricate local‐scale processes. However, much of our understanding of the jet relies on reanalysis, and these along with climate models that produce important projections do not represent these local‐scale processes. We systematically investigate the impact of changing model horizontal and vertical resolution in simulating the Turkana Jet, and associated local and large‐scale processes. We perform simulations to coincide with the Radiosonde Investigation For the Turkana Jet (RIFTJet) campaign, enabling direct model‐sonde comparisons in unprecedented detail. We find that increasing horizontal model resolution significantly increases the strength of the jet throughout the channel by up to 30%, while vertical resolution changes have little impact. Horizontal resolutions finer than 2.2 km produce a nocturnal jet ∼2 m/s stronger than observed but perform better during the day. The elevated inversion, which is strongly tied to the strength of the jet, is much better represented in resolutions as high as 1.1 km, whereas the global model at resolution O(∼10 km) is unable to produce any nocturnal elevated inversion. Predictions of jet strength are improved at higher resolution, indicating an important role of local process given that models inherit the same large‐scale state. Despite further improvements at resolutions finer than 4.4 km, we recommend that 4.4 km is the minimum horizontal resolution required to capture realistic interactions between these processes. Underestimation of the Turkana Jet could cause considerable errors in moisture advection into Africa. Plain Language Summary: The Turkana Jet plays an important role in the meteorology of East Africa across timescales, and owes its existence to both large‐scale patterns in the atmosphere as well as local factors, such as the representation of orography. However, much of our understanding of the jet relies on models with very coarse grid spacing, and climate models that produce important projections in this area are unable to represent these local processes. We systematically investigate the impact of changing model horizontal and vertical resolution in simulating the Turkana Jet and related processes. We perform simulations to coincide with the Radiosonde Investigation for the Turkana Jet (RIFTJet) campaign; enabling direct comparisons to be made with sonde releases in unprecedented detail. We find that experiments with a finer model grid significantly increase the strength of the jet throughout the channel, while changes to the number of vertical levels have little impact. The warmer air that resides around the height of the jet at night, which is strongly tied to the strength of the jet, is much better represented in resolutions as high as 1.1 km, whereas the global model at resolution O(∼10 km) is unable to produce this elevated warm layer. Key Points: Representation of the Turkana Jet and elevated inversions improves in model simulations up to 2 km resolutionThe inversion and jet strength are positively correlated, and this correlation increases with finer grid resolutionIncreased model resolution leads to improved skill in predicting the strength of the Turkana Jet, despite similar large‐scale forcing [ABSTRACT FROM AUTHOR]

Recent advancements in computational technologies have enhanced the importance of meteorological modeling, driven by an increased reliance on weather-dependent systems. This research implemented a lightning data assimilation technique to improve short-term weather forecasts in South America, potentially refining initialization methods used in meteorological operation centers. The main goal was to implement and enhance a data assimilation algorithm integrating lightning data into the WRF model, assessing its impact on forecast accuracy. Focusing on southern Brazil, a region with extensive observational infrastructure and frequent meteorological activity, this research utilized several data sources: precipitation data from the National Institute of Meteorology (INMET), lightning data from the Brazilian Lightning Detection Network (BrasilDAT), GOES-16 satellite images, synoptic weather charts from the National Institute for Space Research (INPE), and initial conditions from the GFS model. Employing the WRF-ARW model version 3.9.1.1 and WRFDA system version 3.9.1 with 3DVAR methodology, the study conducted three experimental setups during two meteorological events to evaluate the assimilation algorithm. These included a control (CTRL) without assimilation, a lightning data assimilation (LIGHT), and an adaptive humidity threshold assimilation (ALIGHT). Results showed that the lightning data assimilation system enhanced forecasts for large-scale systems, especially with humidity threshold adjustments. While it improved squall line timing and positioning, it had mixed effects when convection was thermally driven. The lightning data assimilation methodology represents a significant contribution to the field, indicating that using such alternative data can markedly improve short-term forecasts, benefiting various societal sectors. [ABSTRACT FROM AUTHOR]

Background: Climate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia. Aims: Here we argue that heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought will amplify potential fire behaviour well beyond projections based on calculations of afternoon forest fire danger derived from climate models. Methods: We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves, drawing on examples of dynamical processes driving fire behaviour during the Australian Black Summer bushfires of 2019–20. Results: Key dynamical processes identified include: nocturnal low-level jets, deep, unstable planetary boundary layers and fire–atmosphere coupling. Conclusions: The future scenario we contend is long windows of multi-day fire events where overnight suppression is less effective and fire perimeters will expand continuously and aggressively over multiple days and nights. Implications: Greater overnight fire activity and multi-day events present strategic and tactical challenges for fire management agencies including having to expand resourcing for overnight work, manage personnel fatigue and revise training to identify conditions conducive to unusually active fire behaviour overnight. Effective messaging will be critical to minimise accidental fire ignition during heatwaves and to alert the community to the changing fire environment Heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought are likely to amplify fire behaviour under climate change. We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves using examples from the 2019–20 Australian Black Summer bushfires and examine potential challenges posed for future fire management. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology. [ABSTRACT FROM AUTHOR]

Every year, large quantities of dust are transported from North Africa to the Americas, Europe, and West Asia. The purpose of this study is to analyze four intense and pervasive dust storms that entered the Middle East from Northern Africa. Satellite products, ground-based remote sensing measurements, reanalysis data, and the outputs of the Aire Limitée Adaptation dynamique Développement InterNational-Dust (ALADIN-Dust) and the ICOsahedral Nonhydrostatic weather and climate model with Aerosols and Reactive Trace gases (ICON-ART) forecasting models were synergized. The dust storms originated from different source regions located in the north, northeastern, and central parts of the Sahara Desert. The transport height of the main dust plumes was about 3–5 km, triggered by the westerly zonal winds. The presence of a closed low over the Eastern Mediterranean and the penetration of a deep trough into North Africa at 500 hPa were the main synoptic circulation patterns favoring long-range dust transport during the four dust events. A comparison of aerosol optical depth (AOD) outputs from the two models with satellite data revealed that although both models forecasted dust transport from Africa to the Middle East, they considerably underestimated the AOD values, especially near the dust sources. The ICON-ART model performed slightly better than ALADIN in forecasting these dust storms, and for longer forecasting leading time, although the performance of both models decreased, the superiority of the ICON-ART model became more apparent. [ABSTRACT FROM AUTHOR]

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = − 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(−), ∆AP(+), ∆MWV(+), and ∆ASH(−) at 0–2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact. [ABSTRACT FROM AUTHOR]

The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023). [ABSTRACT FROM AUTHOR]

Northeast Asia experienced unprecedented abrupt warming in the 1990s since the last century. Based on a robust time series and rank frequency evaluation, the Max Planck Institute for Meteorology Grand Ensembles of CMIP5 (MPI-GE5), CMIP6 (MPI-GE6), EC-Earth3 and IPSL-CM6A-LR were identified as the models that best simulate the external forcing and internal variability in observations and represent observations most adequately. The negative-to-positive phase transition of the Atlantic multidecadal variability (AMV), combined with the external forcing, can explain 88% [60%−111%] of the 1990s warming. With prescribed anthropogenic emissions in the near future, a phase shift in the AMV to +2 (-2) standard deviation will amplify (weaken) the warming over Northeast Asia by 37% [29%−49%] (19% [15%−25%]). This highlights the importance of natural climate variability in Northeast Asia's government decision-making and risk management, and emphasizes that only climate models with an adequate representation of forced warming can quantify these contributions correctly. [ABSTRACT FROM AUTHOR]

Wildfires are an escalating global threat, jeopardizing ecosystems and human activities. Among the repercussions in the ecosystem services of burnt areas, there are altered hydrological processes, which increase the risks of flash floods. There is limited research addressing this issue in a comprehensive way, considering pre- and post-fire conditions to accurately represent flood events. To address this gap, we present a novel approach combining multiple methods and tools for an accurate representation of post-fire floods. The 2019 post-fire flood in Kineta, Central Greece is used as a study example to present our framework. We simulated the meteorological conditions that caused this flood using the atmospheric model WRF-ARW. The burn extent and severity and the flood extent were assessed through remote sensing techniques. The 2D HEC-RAS hydraulic–hydrodynamic model was then applied to represent the flood event, using the rain-on-grid technique. The findings underscore the influence of wildfires on flooding dynamics, highlighting the need for proactive measures to address the increasing risks. The integrated multidisciplinary approach used offers an improved understanding on post-fire flood responses, and also establishes a robust framework, transferable to other similar cases, contributing thus to enhanced flood protection actions in the face of escalating fire-related disasters. [ABSTRACT FROM AUTHOR]

A review of the results of Russian studies of the middle atmosphere in 2019–2022, prepared by the Commission on the Middle Atmosphere of the Meteorology and Atmospheric Sciences Section (MASS) of the Russian National Geophysical Committee for the National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics (Germany, 2023), is presented. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

Cloud fraction (CF) significantly affects the short‐ and long‐wave radiation. Its realistic representation in general circulation models (GCMs) still poses great challenges in modeling the atmosphere. Here, we present a neural network‐based (NN‐based) diagnostic scheme that uses the grid‐mean temperature, pressure, liquid and ice water mixing ratios, and relative humidity to simulate the sub‐grid CF. The scheme, trained using CloudSat data with explicit consideration of grid sizes, realistically simulates the observed CF with a correlation coefficient >0.9 for liquid‐, mixed‐, and ice‐phase clouds. The scheme also captures the observed non‐monotonic relationship between CF and relative humidity and is computationally efficient, and robust for GCMs with a variety of horizontal and vertical resolutions. For illustrative purposes, we conducted comparative analyses of the 2006–2019 climatological‐mean cloud fractions among CloudSat, and simulations from the NN‐based scheme and the Xu‐Randall scheme (optimized the same way as the NN‐based scheme). The NN‐based scheme improves not only the spatial distribution of the total CF but also the cloud vertical structure. For example, the biases of too‐many high‐level clouds over the tropics and too‐many low‐level clouds over regions around 60°S and 60°N in the Xu‐Randall scheme are significantly reduced. These improvements are also found to be insensitive to the spatio‐temporal variability of large‐scale meteorology conditions, implying that the scheme can be used in different climate regimes. Plain Language Summary: Cloud fraction (CF) is crucial to cloud climate effects of both reflecting shortwave radiation and absorbing/emitting longwave radiation. However, the simulation of CF in general circulation models (GCMs) has been difficult, because most clouds are smaller than the typical scales of GCM grids and cannot be resolved by the grid‐scale physics, while the physical understanding of sub‐grid processes is still inadequate. Thus, this study uses a data‐driven approach, that is, a neural network, to parameterize the sub‐grid CF in climate models. The database for training and evaluating this NN‐based scheme is obtained by upscaling the CloudSat (quasi‐) observational data and emulating the GCM "grid‐mean" properties required for cloud‐fraction parameterization, minimizing the data‐oriented biases. Moreover, the effects of the GCM horizontal and vertical grid sizes are both considered in the network, increasing the scheme adaptivity for use in GCMs with different resolutions. Results show that the NN‐based scheme correctly predicts observed features of cloud‐fraction variation with cloud condensate content and relative humidity for clouds of different phases and better predicts total cloud‐fraction spatial distribution and cloud vertical structure than the conventional Xu‐Randall scheme. This suggests that the NN‐based scheme has the potential to reduce the biases of cloud radiative effects existing in current GCMs. Key Points: A neural network‐based scheme for parameterizing sub‐grid cloud fraction in climate models is developed using the CloudSat dataThe scheme considers the effects of both horizontal and vertical grid sizes on cloud‐fraction parameterizationThe scheme better predicts total cloud‐fraction spatial distribution and cloud vertical structure than the Xu‐Randall scheme [ABSTRACT FROM AUTHOR]

This review discusses a simple family of models capable of simulating tropical cyclone life cycles, including intensification, the formation of the axisymmetric version of boundary layer shocks, and the development of an eyewall. Four models are discussed, all of which are axisymmetric, f-plane, three-layer models. All four models have the same parameterizations of convective mass flux and air–sea interaction, but differ in their formulations of the radial and tangential equations of motion, i.e., they have different dry dynamical cores. The most complete model is the primitive equation (PE) model, which uses the unapproximated momentum equations for each of the three layers. The simplest is the gradient balanced (GB) model, which replaces the three radial momentum equations with gradient balance relations and replaces the boundary layer tangential wind equation with a diagnostic equation that is essentially a high Rossby number version of the local Ekman balance. Numerical integrations of the boundary layer equations confirm that the PE model can produce boundary layer shocks, while the GB model cannot. To better understand these differences in GB and PE dynamics, we also consider two hybrid balanced models (HB1 and HB2), which differ from GB only in their treatment of the boundary layer momentum equations. Because their boundary layer dynamics is more accurate than GB, both HB1 and HB2 can produce results more similar to the PE model, if they are solved in an appropriate manner. [ABSTRACT FROM AUTHOR]

Observations made in situ by land evaporators are a useful data source for monitoring potential evapotranspiration (PET). The National Institute of Meteorology and Hydrology of Bulgaria operates the conventional small evaporators, which are sensitive to the site's environment, local, and geographic factors. Furthermore, there are just a few sources of reliable long-term observational data available across the region. Therefore, developing a regional map or predicting the PET at a specific location both can benefit from using regional climate models. This problem was addressed by simulating the PET using the Hargreaves-Samani (HS) approach with the regional climate model (RegCM4). The RegCM4 with 25-km grid spacing for the area of Bulgaria from 1988 to 2010 was downscaled using ERA-Interim reanalysis. The original HS formula and its calibrated version were evaluated with respect to the Climate Research Unit (CRU). Results indicated that adjusting the radiation coefficient is far more efficient than adjusting the temperature coefficient. In addition, the calibrated HS formula, as compared to the default HS formula, consistently performs better at predicting the PET with respect to the CRU, as shown by a significantly smaller bias and a more accurate representation of the climatological yearly cycle. As a result, the RegCM4 can be used as one of the potential alternatives to create a regional map of PET of Bulgaria utilizing the calibrated version of the HS equation, both in the historical and under different future scenarios. [ABSTRACT FROM AUTHOR]

Melting of ice at the base of floating ice shelves that fringe the Antarctic ice sheet has been identified as a significant source of uncertainty in sea level rise projections. Part of this uncertainty derives from chaotic internal variability of the coupled ocean-atmosphere system. For numerical ice sheet model projections, this uncertainty has not previously been quantified because of the prohibitive computational expense of running large climate model ensembles. Here, we develop and demonstrate a technique that generates independent realizations of internal climate variability from a single climate model simulation. Building on previous developments in model emulation, this technique uses empirical orthogonal function decomposition and Fourier-phase randomization to generate statistically consistent realizations of spatiotemporal variability fields for the target climate variable. The method facilitates efficient sampling of a wide range of climate trajectories, which can also be incorporated within ice sheet or other physical models to represent feedback processes. [ABSTRACT FROM AUTHOR]

The performance of a set of atmospheric models and meteorological reanalyses in the representation of precipitation days in Antarctica is assessed using ground‐based observations such as a precipitation gauge and a Micro Rain Radar during the Year Of Polar Prediction Special Observing Period at Dumont d'Urville (November 2018–February 2019), East Antarctic coast. The occurrence of precipitation is overall well predicted, but the number of days and intensity with snowfall are overestimated by the models. This is reflected by high values of bias, probability of detection, and false alarm ratios, in particular for reanalyses, due to too frequent simulated precipitating days. The Heidke skill score shows the overall great contribution of the models in the forecasting of precipitating days, and the best performances are achieved by numerical weather prediction models. The chronology is better represented when the models benefit from the data assimilation of in‐situ observations, such as in reanalysis or weather forecasting models. Precipitation amounts at the surface are overestimated by most of the models. In addition, data from a ground‐based radar make it possible to evaluate the representation of the vertical profiles of snowfall rate. We can show that an excessive sublimation in the atmospheric boundary layer can compensate for overly strong precipitation flux in the mid and low troposphere. Therefore, the need to expand the measurement of precipitation across the atmospheric column using radars is highlighted, in particular in Antarctica where the cold cloud microphysics is poorly known and observations are particularly rare. [ABSTRACT FROM AUTHOR]

Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved. [ABSTRACT FROM AUTHOR]

Historical meteorological datasets are indispensable for forming climatic models and the generation of weather forecasts. Such data are core to the training phase of prediction models and may also be harnessed for hydrological and environmental models. GRIB is the most common data format used in meteorology and represents the de facto standard for storing historical weather data. However, GRIB datasets are complex and do not constitute analysis‐ready data without additional preprocessing. The MÉRA dataset of the Met Eireann, the Irish meteorological service, is archetypical of such datasets, emerging from a high‐resolution climatic reanalysis of Irish weather data between 1981 and 2019. This article describes the MÉRA Data Extractor toolkit. This toolkit enables the intuitive, fast extraction and preprocessing of data from this extensive dataset. The toolkit is available as open source and will be of interest to those researching climate modelling in Europe. [ABSTRACT FROM AUTHOR]

Simulation of daily rainfall for the region of Bologna produced by 13 climate models for the period 1850–2100 are compared with the historical series of daily rainfall observed in Bologna for the period 1850–2014, and analysed to assess meteorological drought changes up to 2100. In particular, we focus on annual rainfall data, seasonality and drought events to derive information on the future development of critical events for water resources availability. The results prove that rainfall statistics, including seasonal patterns, are fairly well simulated by models, while the historical sequence of annual rainfall is not satisfactorily reproduced. In terms of meteorological droughts, we conclude that historical data analysis under the assumption of stationarity may depict a more critical future with respect to climate model simulations, therefore outlining important technical indications. [ABSTRACT FROM AUTHOR]

A crucial aspect of regional air quality modelling is the accurate characterisation of local meteorology, as this plays a significant role in the transport and chemical transformation of pollution across a region. This study explores the potential of The Weather Research and Forecasting (WRF) model as an alternative meteorological model to support near-time air quality predictions for the NSW Greater Metropolitan Region (NSW GMR). We develop an optimal WRF configuration for the NSW GMR by downscaling the latest Bureau of Meteorology (BOM) Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction (NWP) data using a three nested domain setup with resolutions of 12km, 4km and 1 km by varying physical parameterisations including two cumulus schemes, two planetary boundary layer schemes, two microphysical schemes, two compatible shortwave and longwave radiation schemes and a standard land surface model. Thus, a set of 16 experiments were executed and evaluated on different days involving high ozone days, bushfire smoke, dust storms and heatwaves. Each simulation was initialised at 12UTC with a forecast horizon of four days, including three hourly updates of boundary conditions. All simulations were evaluated with metrics including Mean Absolute Errors (MAE) and Index of Agreement (IOA) calculated over selected stations operated by NSW Department of Planning, Industry and Environment (NSW DPIE) and BOM for surface and vertical variables (temperature (T), relative humidity (RH), wind speed (WS)) including precipitation (PRECIP) and planetary boundary layer heights (PBLH). Overall, results showed that the best configuration produced acceptable agreements at the surface for T (IOA > 0.8), RH (IOA > 0.7) and WSP (IOA > 0.6). Errors in PBLH were elevated in the mid-boundary layer but was within 100m where vertical profiles of temperature and winds were well simulated. Similarly, experiments with double moment microphysical schemes were better in simulating precipitation. Other performance metrics for the experiments conducted under different case days will also be discussed. [ABSTRACT FROM AUTHOR]

The article presents the life and career of Inez Fung, a prominent climatologist. Born in Hong Kong in 1949, Fung graduated in applied mathematics from the Massachusetts Institute of Technology (MIT) and obtained her Ph.D. in Meteorology in 1977. Throughout her career, Fung has worked on climate modeling, biogeochemical cycles, and climate change, contributing to reports by the Intergovernmental Panel on Climate Change (IPCC). Currently, Fung is a member of the President's Council of Advisors on Science and Technology and has received numerous awards for her work in the field of climatology. [Extracted from the article]

The meteorology community primarily assesses tropical cyclone (TC) forecast skill using track and intensity errors. These metrics are frequently uncorrelated and can offer conflicting information about model performance. Continued improvements in intensity forecasting require improved representation of physical processes over multiple scales, and model verification of TC spatial structure can contribute to these improvements. To date, there are limited studies into forecast model representation of wind fields. More work is needed to better understand model deficiencies in skillfully predicting TC size metrics. In this study, we demonstrate an object‐based approach that can reveal structural differences in TC wind fields. Object‐based methods have been underutilized, and these methods, along with spatial metrics, can serve as additional verification methods for assessing storm structure in both observations and model simulations. Specifically, we illustrate this approach by examining a major difference between the Tiedtke and Kain–Fritsch cumulus parametrization schemes: The Tiedtke scheme includes convective momentum transport while the Kain–Fritsch scheme does not. We create three experiments of Hurricane Isabel (2003) using the Weather Research and Forecasting model using the Kain–Fritsch and Tiedtke cumulus parametrization schemes and an altered Tiedtke scheme with convective momentum transport turned off. Within the three experiments, we generate a small ensemble of four simulations to avoid drawing erroneous conclusions due to growth of numerical noise. Then, we use object‐based methods to measure and compare spatial attributes of the low‐level wind fields to confirm the dominant influence of momentum transport in influencing the TC spatial structure. Our spatial metric approach offers an objective suite of structural attributes that could be useful in diverse applications. A future goal is to use spatial metrics in systematic verification studies of TCs in operational model forecasts and climate model simulations, which may offer great benefit to operational forecasters and numerical model developers. [ABSTRACT FROM AUTHOR]

This short review shows how innovative processing units—including graphical processing units (GPUs)—are used in high-performance computing (HPC) in meteorology, introduces current scientific studies relevant to HPC, and discusses the latest topics in meteorology accelerated by HPC computers. The current status surrounding HPC is distinctly complicated in both hardware and software terms, and flows similar to fast cascades. It is difficult to understand and follow the status for beginners; they need to overcome the obstacle of catching up on the information on HPC and connecting it to their studies. HPC systems have accelerated weather forecasts with physical-based models since Richardson's dream in 1922. Meteorological scientists and model developers have written the codes of the models by making the most of the latest HPC technologies available at the time. Several of the leading HPC systems used for weather forecast models are introduced. Each institute chose an HPC system from many possible alternatives to best match its purposes. Six of the selected latest topics in high-performance computing in meteorology are also reviewed: floating points; spectral transform in global weather models; heterogeneous computing; exascale computing; co-design; and data-driven weather forecasts. [ABSTRACT FROM AUTHOR]

Mount Everest's summit pyramid is the highest obstacle on earth to the wintertime jet-stream winds. Downwind, in its wake, a visible plume can form. The meteorology and composition of the plume are unknown. Accordingly, daily from 1 November 2020 through 31 March 2021 (151 d), we observed real-time images from a geosynchronous meteorological satellite to identify the days plumes formed. The corresponding surface and upper-air meteorological data were collected. The massif was visible on 143 d (95 %), plumes formed on 63 d (44 %) and lasted an average of 12 h. We used the upper-air data with a basic meteorological model to show the plumes formed when sufficiently moist air was drawn into the wake. We conclude the plumes were composed initially of either cloud droplets or ice particles depending on the temperature. The plumes were not composed of resuspended snow. One plume was observed to glaciate downwind. We estimated snowfall from the plumes may be significant. [ABSTRACT FROM AUTHOR]

The objective of this work was to numerically simulate the concentration of atmospheric pollutants PM10 and PM.2.5 and their relationship with meteorological conditions in Metropolitan Lima (Perú). The open source scientific program CCATT-BRAMS (INPE / CPTEC Brazil) was used. The observed data of the meteorological variables and the concentrations of PM10 and PM.2.5 were obtained from the National Service of Meteorology and Hydrology of Perú (SENAMHI). The simulated concentrations of PM10 and PM2.5 were obtained by the numerical program of chemical transport CCATT and the meteorological variables obtained by the numerical model of the atmosphere BRAMS. The results were validated with the observed data. Results show that there is influence of the increase in wind speed and relative humidity on the decreasing in PM10 in both summer and winter. [ABSTRACT FROM AUTHOR]

The current capabilities of the numerical weather prediction (NWP) model to simulate aerosol‐radiation interaction (ARI) impacts on weather prediction are still rarely considered compared with climate models. Here, the NWP model GRAPES_CUACE is used to evaluate the role of the ARI in meteorology prediction at the weather scale in China. The results show that the online calculation of ARI in the model can extensively improve the meteorology prediction accuracy involving temperature, wind, and pressure at most vertical levels in relatively clean, light, and heavy pollution stages. This improvement significantly occurs in the meteorology factors below 950 hPa prediction such as temperature at 2 m, 1,000, and 950 hPa, and mean sea level pressure, particularly in heavy pollution areas and stages. Unlike temperature, the improvement of ARI in the predicted wind at the height of the boundary layer is more significant than near‐surface. However, this improvement declines when the low‐cloud exists. Plain Language Summary: Aerosol‐radiation interaction (ARI) has been sufficiently considered in the climate forecast system, but it is not enough recognized in the numerical weather prediction (NWP) system. We want to determine what role the ARI plays in weather forecast from the current NWP system in areas with relatively heavy aerosol pollution. We find that the ARI can significantly improve the accuracy of the weather forecast. At the same time, the reduced improvement during the low‐cloud weather period may help us to understand the limitations of the ARI effect in the current NWP system. Key Points: Aerosol‐radiation interaction (ARI) can improve the predicted vertical structure of meteorological factors even in relatively clean stagesThis improvement by ARI significantly occurs in the lower meteorology factors prediction, particularly in heavy pollution areas and stagesThe ARI's improvement on model meteorology prediction accuracy declines when the low‐cloud exists [ABSTRACT FROM AUTHOR]

Following collapses of the neighboring Larsen A and B ice shelves, Larsen C has become a focus of increased attention. Determining how the prevailing meteorological conditions influence its surface melt regime is of paramount importance for understanding the dominant processes causing melt and ultimately for predicting its future. To this end, a new, high‐resolution (4 km grid spacing) Met Office Unified Model (MetUM) hindcast of atmospheric conditions and surface melt processes over the central Antarctic Peninsula is introduced. The hindcast is capable of simulating observed near‐surface meteorology and surface melt conditions over Larsen C. In contrast with previous model simulations, the MetUM captures the observed east‐west gradient in surface melting associated with foehn winds, as well as the interannual variability in melt shown in previous observational studies. As exemplars, we focus on two case studies—the months preceding the collapse of the Larsen B ice shelf in March 2002 and the high foehn, high melt period of March‐May 2016—to test the hindcast's ability to reproduce the atmospheric effects that contributed to considerable melting during those periods. The results suggest that the MetUM hindcast is a useful tool with which to explore the dominant causes of surface melting on Larsen C. Plain Language Summary: Scientists are concerned about floating ice shelves on the Antarctic Peninsula because several shelves have collapsed there in recent decades, due partly to melting at the surface. However, our understanding of what causes these ice shelves to melt is limited by the lack of observations in the region, and so numerical models are an extremely useful tool to explore melt processes. This study showcases a new high‐quality model data set that is able to capture the major patterns of surface melting and atmospheric conditions over ice shelves on the Antarctic Peninsula. It represents an improvement on previous studies and can therefore be used to examine melt and meteorology on ice shelves like Larsen C. The ability of the hindcast to capture these processes is illustrated via two case studies—the period just before the collapse of the Larsen B ice shelf in March 2002, and a period in March‐May 2016 when exceptionally high melt and intense foehn winds were observed on the Larsen C ice shelf. Simulations of reasonable accuracy suggest that the hindcast is suitable for exploring the causes of ice shelf surface melting in the region. Key Points: We present a new high‐resolution, multidecadal hindcast of atmospheric conditions, and surface melt processes over the Larsen C ice shelfThe MetUM hindcast captures the expected location, frequency, and interannual variability of foehn events on Larsen CThe hindcast captures the foehn‐induced distribution and interannual variability of surface melt patterns on Larsen C [ABSTRACT FROM AUTHOR]

Quantifying the relative importance of the atmospheric drivers of surface melting on the Larsen C ice shelf is critical in the context of recent and future climate change. Here, we present analysis of a new multidecadal, high‐resolution model hindcast using the Met Office Unified Model, described in Part 1 of this study. We evaluate the contribution of various atmospheric conditions in order to identify and rank, for the first time, the most significant causes of melting over the recent past. We find the primary driver of surface melting on Larsen C is solar radiation. Foehn events are the second most important contributor to surface melting, especially in nonsummer seasons when less solar radiation is received at the surface of the ice shelf. Third, cloud influences surface melting via its impact on the surface energy balance (SEB); when the surface temperature is warm enough, cloud can initiate or prolong periods of melting. Lastly, large‐scale circulation patterns such as the Southern Annular Mode (SAM), El Niño Southern Oscillation, and Amundsen Sea Low control surface melting on Larsen C by influencing the local meteorological conditions and SEB. These drivers of melting interact and overlap, e.g., the SAM influences the frequency of foehn, commonly associated with leeside cloud clearances and sunnier conditions. Ultimately, these drivers matter because sustained surface melting on Larsen C could destabilize the ice shelf via hydrofracturing, which would have consequences for the fate of the ice shelf and sea levels worldwide. Plain Language Summary: In order to predict the future of the largest remaining ice shelf on the Antarctic Peninsula—Larsen C—we must understand what is causing it to melt at the surface. We use results from a new model data set to explore which causes of melting are the most important. Our results show that the most dominant factor is solar radiation, especially in summer, while relatively warm, dry foehn winds are the second most important cause of melting. Foehn winds are an especially significant cause of melting in nonsummer seasons. The third driver of surface melting is cloud, because clouds can affect how much energy is received at the surface of the ice shelf. When it is warm enough, clouds can initiate or sustain melting. The final cause of melting is large‐scale atmospheric circulation patterns, which can establish the conditions that promote melting, such as sunny, cloudy, or foehn periods. These melt drivers interact with one another and can compound or dampen the effects of other causes of melting. These melt drivers matter because surface melt could cause this ice shelf to collapse, and therefore indirectly contribute to sea level rise. Key Points: The amount of surface melting on Larsen C is driven mostly by sunny conditions, followed by foehn events, cloud, and large‐scale circulationDeep Amundsen Sea Low, positive Southern Annular Mode, and El Niño conditions enhance surface meltingDrivers of surface melting overlap and interact [ABSTRACT FROM AUTHOR]

With increasing interest in urban meteorology and related services, the need to appropriately represent the urban environment in climate/weather models is rising. These regional weather/climate models typically use a km‐scale horizontal grid, which is insufficient to resolve the flow around buildings. Effects of the urban environment on the atmosphere above are represented through a bulk approach using the Urban Canopy Parametrization (UCP) schemes. Existing UCPs usually use the repeating canyon–roof representation that assumes homogeneous distribution of buildings within the grid box. It is commonly accepted that the assumption of homogeneity holds at km‐scale grid resolution but whether it also holds at sub‐km scale, where the regional models are increasingly approaching, is questionable. For this reason, among others, the resolution ranges from a few hundred metres to tens of metres (i.e. building‐resolving scales) is termed the building grey zone in the existing literature. This work shows that the assumption of homogeneity indeed does not hold at the building grey zone for the city‐state Singapore. To understand the possible influences of the use of UCPs at scales from the building grey zone to the conventional mesoscale, we use an urban‐grid method that allows us to estimate the parametrized fluxes from a typical UCP at varying resolutions over the urban landcover while keeping the same atmospheric model grid. Numerical results show that different urban‐grid resolutions yield variations in the near‐surface temperature and wind to a maximum of 0.5 K and 1 m·s−1. Their impact on the boundary‐layer parameters is found to be limited. Although these near‐surface variations are small, they are comparable to the near‐surface scaling variables and are thus physically significant. [ABSTRACT FROM AUTHOR]

The Community Atmosphere Model (CAM) has been ported, redesigned, and scaled to the full system of the Sunway TaihuLight, and provides peta-scale climate modeling performance. Based on a novel domain decomposition method, we have fully optimized the complete model code by using both OpenACC refactoring and more aggressive and finer-grained Athread approaches. The Athread approach enables us to achieve exceptional memory control and usage, efficient vectorization, and sophisticated utilization of the thread-level communication mechanism. We have also further refined the load-balance behaviors towards ultra-large-scale numerical simulation. By combining all these novelties, we achieved a simulation speed of 7.2 and 25.6 simulation-year-per-day (SYPD) for global 25-km and 100-km resolution, respectively (1.2- to 2.2-fold improvements over previous efforts), and a sustainable double-precision performance of 3.3 PFlops for a 750-m global simulation when using 10075000 cores. [ABSTRACT FROM AUTHOR]

Objective Variation in crop yield is the consequence of many natural and anthropogenic factors, and disentangling their impacts is important for improving agricultural management but difficult. The purpose of this paper is to propose and compared different methods to isolate the impacts of meteorological change on crop yield based on long time series of maize yield in Yellow River-water irrigated region in Ninxia province of China. Method The analysis is based on maize yield measured from1988 to 2019 in 6 counties located in the Yellow River-watered irrigation areas. We compared three methods for the separation: five -year moving average method, quadratic exponential smoothing method, and five-point quadratic smoothing method. The consistent correlation coefficient, trend coincidence conformity analysis method, consistency of climate change characteristics, which lead to the same rise-fall in meteorological yield, were used as the evaluation criteria. Their applicability and rationality were compared and analyzed. All methods were calibrated based on the relationship between meteorological factors and maize yield. Result All methods can fit the yield trend well. Compared with the average yield trend, the consistency correlation coefficients of all three methods were >0.5, suggesting that there was no significant difference between these methods for fitting the yield trend. The advantage of the quadratic exponential smoothing method and the five-point quadratic smoothing method is that they accurately describe the change in the yield as affected by national productivity and national policy. The change in the yield due to meteorological factors estimated by the five-point quadratic smoothing method described the effect of inter-annual meteorological factors better, and its associated meteorological yield model is able to describe the relationship between the meteorological factors and the maize yield. Conclusion omprehensive analysis showed that the five-point quadratic smoothing method modeled the yield change due to meteorological factors better than the other two methods. [ABSTRACT FROM AUTHOR]

Variability in the ionosphere during the 2020–2021 sudden stratospheric warming (SSW) is investigated using a combination of Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) observations and the Whole Atmosphere Community Climate Model with thermosphere–ionosphere eXtension (WACCM-X) simulations. The unprecedented spatial–temporal sampling of the low latitude ionosphere afforded by COSMIC-2 enables investigating the short-term (<5 days) variability in the ionosphere during the SSW event. The COSMIC-2 observations reveal a reduction in the diurnal and zonal mean ionosphere total electron content (ITEC) and reduced amplitude of the diurnal variation in the ionosphere during the SSW. Enhanced ITEC amplitudes of the semidiurnal solar and lunar migrating tides and the westward propagating semidiurnal tide with zonal wavenumber 3 are also observed. The WACCM-X simulations demonstrate that these variations are driven by variability in the stratosphere–mesosphere during the 2020–2021 SSW event. The results show the impact of the 2020–2021 SSW on the mean state, diurnal, and semidiurnal variations in the ionosphere, as well as the capabilities of the COSMIC-2 mission to observe short-term variability in the ionosphere that is driven by meteorological variability in the lower atmosphere. [ABSTRACT FROM AUTHOR]

Atmospheric aerosol size and abundance influence radiative effects and climate change. To date, efforts to constrain global climate models' radiative forcing with in situ aerosol observations have been hamstrung by uncertainty. One source of error, the regional "representation error," arises when accurate but sparse single‐point measurements of atmospheric aerosol distributions are compared with a model value, assuming that the single‐point measurement is representative of the model domain. The Portable Optical Particle Spectrometer network in the Southern Great Plains (POPSnet‐SGP) campaign has demonstrated that a network of nearly autonomous aerosol instruments operating at ambient temperature and relative humidity (with low measurement error) may be used to quantify measurement representation error and investigate the factors introducing heterogeneity in aerosol distributions across a rural, continental background region. Measurements were made using Portable Optical Particle Spectrometer (POPS) instruments at several sites for five months across the Department of Energy's Aerosol Radiation Measurement Southern Great Plains (ARM‐SGP) User Facility in the central USA. Measurement representation error decreased with longer averaging periods (20%–40% between 1 s and 1 day), varied between sites by 10%–20% for aerosol concentration 140–2,500 nm in diameter (N_140), and was higher for aerosols >400 nm in diameter (N_400). Our measurements also show the influence of local meteorology on aerosol surface area (A_140) and size distributions: A_140 is positively correlated with wind speed and relative humidity, negatively correlated with precipitation, and lower given westerly winds. We conclude that the POPSnet approach provides considerably more insight into the spatial variability in the aerosol population that can be used to constrain climate models than would be available from similar networks of PM 2.5 monitors. Plain Language Summary: Global climate models have long struggled to predict the radiative effects of aerosols on climate. In addition to measurement error and model error, model‐observation comparisons may suffer from error related to a sample's representativeness of the aerosol population. This study outlines an approach to quantify the representation error, using the Southern Great Plains (SGP) as a testbed, and find that to an extent, observed differences are related to local meteorology. Key Points: Measurement networks can quantify regional representation error and reduce uncertainty in aerosol microphysicsRepresentation error varies between sites, decreases over longer averaging periods, and is higher for larger accumulation mode aerosolsLocal meteorology introduces heterogeneity in aerosol surface area and size distributions across a rural, continental background region [ABSTRACT FROM AUTHOR]

A previous study on the use of nudging in EAMv1 had an unresolved issue, namely a simulation nudged to EAMv1's own meteorology showed non-negligible deviations from the free-running baseline simulation over some of the subtropical marine stratocumulus and trade cumulus regions. Here, we demonstrate the deviations can be substantially reduced by two changes in the nudging implementation: first, revising the sequence of calculations in a nudged EAM simulation to improve consistency with the free-running baseline; second, increasing the frequency of constraining data from 6-hourly to 3-hourly to better capture strong sub-diurnal variations. The resulting improvements in climate representativeness motivate an investigation on the potential benefits of using newer reanalysis products with higher data frequency in nudged simulations that aim at capturing observed weather events. Simulations nudged towards 6-hourly ERA-Interim reanalysis or 6-hourly, 3-hourly, and hourly ERA5 reanalysis are evaluated against EAMv1's own climatology, global-scale satellite retrievals of outgoing longwave radiation and precipitation, as well as in-situ measurements of air temperature, humidity, and winds from the Atmospheric Radiation Measurement (ARM) user facility. Our overall recommendation is to use the revised sequence of calculation and 3-hourly data from ERA5 for the nudged simulations. The anthropogenic aerosol effects in various EAMv1 simulations are evaluated. For estimating the global mean effect, the source of constraining data has a relatively small impact. Consistent with conclusions from previous studies, in order to obtain estimates of global mean aerosol effects that are consistent with results from the free-running baseline, nudging the horizontal winds but not air temperature is recommended. [ABSTRACT FROM AUTHOR]

This paper describes the benefits of using more reliable local climate scenarios to analyse hydrological responses. It assumes that Regional Climate Model (RCM) simulations are more reliable when they provide better approximations to the historical basic and drought statistics after applying a bias correction to them. We have investigated whether the best solutions in terms of their approximation to the local meteorology may also provide the best hydrological assessments. We have carried out a classification of the corrected RCM simulations used for both approximations. This has been applied in the Cenajo basin (south-eastern Spain), where we show that the best approximations of the historical meteorological statistics also provide the best approximations for the hydrological statistics. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP8.5 emission scenario. The two selected RCMs predict significant changes in mean precipitation (-31.6 % and -44.0 %) and mean temperature (+26.0 % and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts. These two RCMs also predict higher changes in mean streamflow (-43.5 % and -57.2 %) and hydrological droughts. The two RCMs also predict worrying changes in streamflow (-43.5 % and -57.2 %) and hydrologically extreme droughts: frequency (from 3 to 11 for the first model and 8 events for the second model), length (8.3 to 15.4 and 29.6 months), magnitude (from 3.98 to 11.84 and 31.72 SSI), and intensity (0.63 to 0.90 and 1.52 SSI). [ABSTRACT FROM AUTHOR]

The forthcoming spaceborne Ice Cloud Imager (ICI) radiometer has 11 channels in the millimeter (mm) and sub-mm wave range from 183 up to 664 GHz. At some of these frequencies, the atmosphere is very opaque due to strong gaseous and cloud extinction, precluding the observation of the surface. We aim at investigating how to evaluate the ICI channels geolocation error using surface landmark targets. The most transparent ICI channels, i.e., those around 183.3 ± 7.0 GHz at vertical (V) polarization (ICI-1) and around 243.2 ± 2.5 GHz (ICI-4) at horizontal (H)/ V polarization, are considered. Starting from a previous work, we extend the database of the surface landmark targets to cover boreal and austral dry seasons at various latitudes. For testing the geolocation approach, we use satellite Special Sensor Microwave Imager/Sounder (SSMIS) available data at 183.31 ± 6.6 GHz at H-polarization during 2017, obtaining an overall mean error of about 5.0 km and standard deviation of about 2.2 km, well within the ICI geolocation error assessment specifications. Since no imagers are available at 243 GHz, we extrapolate SSMIS data to 243.2 ± 2.5 GHz using a model-based neural-network approach, named Blended Artificial-neural-network Microwave Imager Simulator (BAMIS). The latter is trained by radiative-transfer simulations and global-scale atmospheric reanalyses data as well as SSMIS data. Results confirm that the proposed approach can be successfully exploited for ICI-4 geolocation error assessment at 243.2 ± 2.5 GHz, with results close to those obtained for the SSMIS 183.31 ± 6.6-GHz channel. [ABSTRACT FROM AUTHOR]

Specified dynamics schemes are ubiquitous modeling tools for isolating the roles of dynamics and transport on chemical weather and climate. They typically constrain the circulation of a chemistry–climate model to the circulation in a reanalysis product through linear relaxation. However, recent studies suggest that these schemes create a divergence in chemical climate and the meridional circulation between models and do not accurately reproduce trends in the circulation. In this study we perform a systematic assessment of the specified dynamics scheme in the Community Earth System Model version 2, Whole Atmosphere Community Climate Model version 6 (CESM2 (WACCM6)), which proactively nudges the circulation toward the reference meteorology. Specified dynamics experiments are performed over a wide range of nudging timescales and reference meteorology frequencies, with the model's circulation nudged to its own free-running output – a clean test of the specified dynamics scheme. Errors in the circulation scale robustly and inversely with meteorology frequency and have little dependence on the nudging timescale. However, the circulation strength and errors in tracers, tracer transport, and convective mass flux scale robustly and inversely with the nudging timescale. A 12 to 24 h nudging timescale at the highest possible reference meteorology frequency minimizes errors in tracers, clouds, and the circulation, even up to the practical limit of one reference meteorology update every time step. The residual circulation and eddy mixing integrate tracer errors and accumulate them at the end of their characteristic transport pathways, leading to elevated error in the upper troposphere and lower stratosphere and in the polar stratosphere. Even in the most ideal case, there are non-negligible errors in tracers introduced by the nudging scheme. Future development of more sophisticated nudging schemes may be necessary for further progress. [ABSTRACT FROM AUTHOR]

The weather research forecasting (WRF) model is a mesoscale numerical weather prediction system developed by the United States (US) National Centre for Atmospheric Research (NCAR) and is utilised primarily for weather research and forecasting applications. However, output from WRF can be adapted for air dispersion modelling with suites such as CALPUFF and AERMOD able to use the WRF data in their meteorological pre-processors. One of the key data inputs for WRF is the forcing meteorology data, which is generally in the form of reanalysis data from global scale forecasting models. Many meteorological datasets exist that can be used to drive the WRF model. Some examples of these include reanalysis datasets from the US National Centres for Environmental Prediction (NCEP), Global Data Assimilation System (GDAS), and ERA-Interim developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets are available at various spatial and temporal resolutions, and as such have varying skill and accuracy in predicting local meteorology. A sensitivity analysis was conducted comparing WRF model results for South East Queensland utilising various meteorology input datasets and compared directly to surface meteorology monitoring data from the Australian Bureau of Meteorology (BoM). [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy. [ABSTRACT FROM AUTHOR]

Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SM×8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5‐SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best‐performing MF subsets for predicting LCF. NN ensembles are used to (a) confirm the performance of selected MF subsets, (b) to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and (c) to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SM×8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SM×8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF‐MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SM×8 are greater than those produced by E3SM72 but are still different from those in ERA5‐SSF. In general, the shift in MFs dominates the difference in the LCFs. Plain Language Summary: Marine warm low clouds are critical for both present day and future climate because they reflect a lot of solar energy back to space. To make more reliable projections of our changing climate, scientists need to improve these clouds in climate models. One question that scientists ask is, why do the climate models predict so much less marine warm low cloud cover than the satellites see? Is it because the models misrepresent the meteorology (like temperature and humidity) or because they are not able to produce enough clouds even if they predict the meteorology well. In this work, we use neural networks, a machine learning technique, to answer these questions. We find that our recent efforts to improve a climate model help the model produce more marine warm low clouds given the same meteorology; these efforts also lead to changes in the meteorology predicted by the model. Further model improvements are needed to bring the model predictions closer to the observations. Key Points: Neural network ensembles are able to detect how different meteorological factors are between E3SM, E3SM‐FIVE, and observationsE3SM‐FIVE high vertical resolution simulation improves marine warm low clouds fractions given the same meteorological factorsMeteorological factors between the three data sets are large enough to dominate the differences in marine warm low cloud fractions [ABSTRACT FROM AUTHOR]

Over the last 30 years, the Computational Science Graduate Fellowship (CSGF) program has played an integral role in preparing a large and diverse community of computational scientists to push the limits of high-performance computing (HPC). To celebrate the CSGF program's enduring influence, I share my perspective from the climate modeling community, which has used HPC to better understand the earth's climate system. While the benefits of HPC in climate science have been enormous, rapid computing advances have brought new challenges. One difficulty is quality assurance—ensuring that large and complex codes running on multiple platforms are correct. A second is mitigating the increasingly large data volumes. Here, I describe our software quality assurance framework and strategies to reduce climate data volumes. In these complementary efforts, our focus has been preserving the scientific integrity of the simulation output data, a priority shared across HPC model simulation disciplines. [ABSTRACT FROM AUTHOR]