Preprint of paper Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses, published in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 109-119, Jan. 2018. doi: 10.1109/TVCG.2017.2745178 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8019883&isnumber=8165924

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat-related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP-BEM climate model. We find that on average cool roofs most effectively reduce temperatures (~ -1.2°C), outperforming green roofs (~ 0°C), solar panels (~ -0.3°C) and street level vegetation (~ -0.3°C). Application of air conditioning across London increase air temperatures by ~ +0.15°C but related energetic consumption could be covered by energy production from solar panels. Current realistic deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision-making.

E-region models have traditionally underestimated the ionospheric electron density. We believe that this deficiency can be remedied by using high-resolution photoabsorption and photoionization cross sections in the models. Deep dips in the cross sections allow solar radiation to penetrate deeper into the E-region producing additional ionization. To validate our concept, we perform a study of model electron density profiles (EDPs) calculated using the Atmospheric Ultraviolet Radiance Integrated Code (AURIC; \citeA{strickland1999atmospheric}) in the E-region of the terrestrial ionosphere. We compare AURIC model outputs using new high-resolution photoionization and photoabsorption cross sections, and solar spectral irradiances during low solar activity with incoherent scatter radar (ISR) measurements from the Arecibo and Millstone Hills observatories, COSMIC-1 observations, and outputs from empirical models (IRI-2016 and FIRI-2018). AURIC results utilizing the new high-resolution cross sections reveal a significant difference to model outputs calculated with the low-resolution cross sections currently used. Analysis of AURIC EDPs using the new high-resolution data indicate fair agreement with ISR measurements obtained at various times at Arecibo but very good agreement with Millstone Hills ISR observations from $\sim96$ km to $140$ km. However, discrepancies in the altitude of the E-region peak persist. High-resolution AURIC calculations are in agreement with COSMIC-1 observations and IRI-2016 model outputs between $\sim105$ km and $140$ km while FIRI-2018 outputs underestimate the EDP in this region. Overall, AURIC modeling shows increased E-region electron densities when utilizing high-resolution cross sections and high-resolution solar irradiances, and are likely to be the key to resolving the long standing data-model discrepancies.

This study analyzes fire-induced winds from a wind-driven fire (Thomas Fire) and a plume-dominated fire (Creek Fire). Two numerical experiments, one without the fire present and the other with the fire, were used. The fire-induced perturbations were then estimated by subtracting a variable value in the “No Fire Run” from the “Fire Run” (Fire - No Fire). For this study, spatial and temporal variability of winds, geopotential height, and convergence were analyzed. Furthermore, cloud water mixing ratio, precipitation, and fuel moisture were analyzed during the Creek Fire to assess fire-induced rainfall and its impact on fuel moisture. It was found that the wind-driven Thomas Fire created more widespread and generally stronger fire-induced winds than the plume-dominated Creek Fire. In addition, fire-induced wind speeds during the Creek Fire followed a diurnal cycle, while the Thomas Fire showed much less temporal variability. When analyzing geopotential height, the results were very similar to other idealized simulations. A localized low-pressure region was observed in front of the fire front, with a preceding high-pressure area. When analyzing precipitation, it was found that the fire increased precipitation accumulation in the area surrounding the active fire. This created an increase in fuel moisture which could have helped locally decelerate the fire spread. Further research into the processes behind fire-atmosphere interactions will lead to a better understanding of fire behavior and the extent to which these interactions can impact the fire environment. These studies will help assess the limitations of uncoupled operational models and improve fire modeling overall.

Ocean-atmosphere coupled climate models struggle to produce a single northern hemisphere intertropical convergence zone (ITCZ), and instead simulate ITCZ bands in both hemispheres. This “double ITCZ’ bias can negatively impact representations of large-scale modes of variability, such as the Madden-Julian oscillation and El Ni\ no–Southern Oscillation. A new method to estimate model fluxes that would have been obtained with the COARE3.0 bulk flux algorithm indicates that twelve of fourteen CMIP6 models overestimate surface fluxes in the ITCZ region, suggesting that biases rooted in model flux algorithms may contribute to ITCZ biases. This finding is supported by atmosphere-only simulations of two models where the original flux algorithms are replaced with the COARE3.0 algorithm. In the experiments, precipitation root mean square errors in the double ITCZ region were reduced by 26\% and 15\%, respectively. We interpret these findings through the lenses of global energy constraints and convection-boundary layer interactions.

Compound dry and hot extremes (CDHE, such as recent summers 2015, 2018 and 2022 in Europe) have wide ranging impacts: Heat exacerbates moisture shortages during dry periods whereas water demand rises. Climate change will likely increase the intensity, frequency, and duration of CDHE events in Europe. However, current studies focus on drivers and impacts in coarse-resolution global climate models and likely miss spatial details of CDHE characteristics. To overcome this issue, we exploit a regional 50-member single-model initial condition large ensemble (SMILE) at 12 km spatial resolution. Hence 1000 model years per 20 year-periods provide an extensive database of CDHE and robust estimations of their occurrence changes across Europe in high geographical detail. CDHE occurrences are investigated in a current climate and at two global warming levels (+2 °C, +3 °C). We identify Northern France, Southern Germany, Switzerland, Southern Ireland, and the western coasts of the Black Sea with currently low CDHE frequencies as emerging hotspots. These regions experience a tenfold occurrence increase under global warming conditions. Apart from Western Europe, temperature is the dominant contributor to frequency increases. Furthermore, tail dependencies strengthen in regions with high CDHE frequency increases. In European agricultural areas, soil moisture shows very strong negative correlations with CDHE extremeness. Last, our results suggest a halving of CDHE in a +2 °C world compared to a +3 °C world, highlighting the necessity of climate mitigation with respect to this hazard type.

Accurate seasonal precipitation forecasts, especially for extreme events, are crucial to preventing meteorological hazards and its potential impacts on national development, social stability, and security. However, the intensity of summer precipitation is often significantly underestimated in many current dynamical models. This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks (CycleGAN) to enhance the seasonal forecast skill of the Nanjing University of Information Science & Technology Climate Forecast System (NUIST-CFS1.0) in predicting June-July-August precipitation in southeastern China. The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatial-temporal distribution of summer precipitation than traditional quantile mapping (QM) method. Due to the use of unpaired day-to-day correction models, we can pay more attention to the frequency, intensity, and duration of extreme precipitation events in the climate dynamical model forecast. This study expands the potential applications of deep learning models to improving seasonal precipitation forecasts.

Climate models robustly project acceleration of the Brewer-Dobson circulation (BDC) in response to climate change. However, the BDC trends derived from comprehensive models do not fully match observations. Additionally, the changing structure of the troposphere and stratosphere has received increasing attention in recent years and to which extent vertical shifts of the circulation are driving the acceleration is under debate. In this study, we present a novel method that enables the attribution of circulation changes to individual kinematic factors. Using this method allows to study the advective BDC trends in unprecedented detail and sheds new light into discrepancies between different datasets (reanalyses and models) at the tropopause and in the lower stratosphere. Our findings provide insights into the reliability of model projections of BDC changes and offer new possibilities for observational constraints.

To aid development of sub-grid scale (SGS) parameterizations for Earth system models which consider heterogeneity in land-surface fields and land-atmosphere coupling, results from large-eddy simulations of 92 shallow convection cases over the Southern Great Plains are presented and analyzed. Each case is simulated with heterogeneous surface fields obtained from an offline field-scale land-surface model, and with spatially homogeneous surface fields with the same domain-wide mean value. By comparing corresponding heterogeneous and homogeneous cases, it is found that turbulent kinetic energy and liquid water path has a high correlation with the spatial variance of the surface heat flux fields. By further comparing the source of this correlation over the range of wavelengths in the surface fields, it is found that the majority of the heterogeneous land-atmosphere coupling is contained in wavelengths of order 10 km and larger, suggesting an encouraging degree of feasibility of including land-surface heterogeneity in global-scale SGS parameterizations.

Numerical weather prediction (NWP) and climate models still struggle to correctly predict and represent atmospheric blocking over the European region (EuBL). In recent years, there has been growing evidence that latent heat release in midlatitude weather systems such as warm conveyor belts (WCBs) contribute significantly to the onset and maintenance of blocking anticyclones. In this study, we show that for the European Centre for Medium-Range Weather Forecast’s IFS reforecasts in extended winter (1997–2017) WCB activity around EuBL onsets becomes challenging to predict in pentad 3 (10–14 days) and beyond. This is in line with the short overall WCB forecast skill horizon of around 10 days and partly explains low EuBL skill in NWP models. However, we also show cases in which accurate WCB and EuBL forecasts are possible even in pentad 4 (15–19 days). These cases are associated with accurate WCB forecasts over the North Atlantic and North Pacific pointing towards a teleconnection between the two. Lastly, we find that WCB activity over the North Atlantic emerges way before the block is established and different pathways into EuBL exist in the reforecasts which are characterised by a westward shift of the main WCB inflow and outflow region compared to reanalysis. We conclude that despite intrinsic limits of predictability there is room to improve forecasts of EuBL onset by improving the representation of WCB activity in NWP models.

Ziping Zuoa, Jimmy C.H. Funga,b, Zhenning Lia,*, Yiyi Huangd, Mau Fung, Wonga, Alexis K.H. Laua,c, Xingcheng Luea Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinab Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinac Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinad Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USAe Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, ChinaCorresponding author: Zhenning Li,lzhenn@ust.hkABSTRACTRecent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late 21st century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7–0.8 °C and 0.9–1.1 °C, respectively. In the 2090s, the mean difference will be 0.5–3.1 °C and 0.7–3.4 °C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. Compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4 °C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2–0.4 °C in all three scenarios. In the 2090s, the increase in these values will be 23 days and 36.0 °C in SSP5-8.5. Moreover, a 10-year extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s.SIGNIFICANCE STATEMENTPearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure.

Quantitative precipitation forecasting in numerical weather prediction (NWP) models rely on physical parameterization schemes. However, these schemes involve considerable uncertainties due to limited knowledge of the mechanisms involved in the precipitating process, ultimately leading to degraded precipitation forecasting skills. To address this issue, our study proposes using a Swin-Transformer based deep learning (DL) model to quantitatively map fundamental variables solved by NWP models to precipitation maps. Our results show that the DL model effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Based on two case studies, we show for the first time that explicitly resolving polar lows in a global climate model (ICON-Sapphire) with a high resolution of 2.5 km in all components (atmosphere, ocean, sea ice and land) leads to strong heat loss from the ocean near the sea ice edge and from leads and polynyas in the ice cover. Heat losses during marine cold air outbreaks triggered by polar lows lead to the formation of dense water in the Iceland and Greenland Seas that replenishes the climatically important Denmark Strait Overflow Water (DSOW). Further heat losses and the rejection of brine during ice formation in polynyas, such as the Sirius Water Polynya in northeast Greenland, contribute to the formation of dense water over the Greenland shelf. In the Labrador Sea, polar lows intensify cold air outbreaks from the sea ice and quickly deepen the ocean mixed layer by 100 m within two days. If mesoscale polar lows and kinematic features in the sea ice are not resolved in global climate models, heat loss and dense water formation in (sub-)polar regions will be underestimated.

Central Asia (CA) is experiencing rapid warming, leading to more Extreme precipitation events (EPEs). However, the anticipated changes in cropland and population exposure to EPEs are still unexplored. In this study, projected changes in EPEs characteristics, as well as cropland and population exposure from EPEs are quantified using global climate model simulations. Our findings reveal a significant increase in the exposure of cropland and population to extreme precipitation over time. Specifically, under the high-emission SSP5-8.5 future pathway, the amount, frequency, intensity, and spatial extent of extreme precipitation in CA are projected to considerably amplify, particularly in the high mountain regions. Under the SSP5-8.5 scenario, cropland exposure in CA increases by 46.4%, with a total cropland exposure of approximately 190.7 million km² expected between 2021 and 2100. Additionally, under the SSP3-7.0 scenario, population exposure in CA increases by 92.6%, resulting in a total population exposure of about 48.1 billion person-days during the same period. The future maximum centers of exposure are concentrated over northern Kazakhstan and the tri-border region of Tajikistan, Kyrgyzstan, and Uzbekistan. Notably, the climate effect is more dominant than the other effects, whereas changes in population effect contribute to the total change in population exposure. Given the heterogeneous distribution of population and cropland in CA, it is imperative for the countries in the region to implement effective measures that harness extreme precipitation and cope with the impacts of these extreme climate events.

Reliable boundary-layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in climate models using bulk formulae based on the Monin-Obukhov Similarity Theory (MOST), sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine-learning (ML) flux parametrizations, using an advanced polar-specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug-in Fortran implementation of the neural networks for use in climate models.

As climate modellers prepare their code for kilometre-scale global simulations, the computationally demanding radiative transfer parameterization is a prime candidate for machine learning (ML) emulation. Because of the computational demands, many weather centres use a reduced spatial grid and reduced temporal frequency for radiative transfer calculations in their forecast models. This strategy is known to affect forecast quality, which further motivates the use of ML-based radiative transfer parameterizations. This paper contributes to the discussion on how to incorporate physical constraints into an ML-based radiative parameterization, and how different neural network (NN) designs and output normalisation affect prediction performance. A random forest (RF) is used as a baseline method, with the European Centre for Medium-Range Weather Forecasts (ECMWF) model ecRad, the operational radiation scheme in the Icosahedral Nonhydrostatic Weather and Climate Model (ICON), used for training. Surprisingly, the RF is not affected by the top-of-atmosphere (TOA) bias found in all NNs tested (e.g., MLP, CNN, UNet, RNN) in this and previously published studies. At lower atmospheric levels, the RF is able to compete with all NNs tested, but its memory requirements quickly become prohibitive. For a fixed memory size, most NNs outperform the RF except at TOA. For the best emulator, we use a recurrent neural network architecture which closely imitates the physical process it emulates. We additionally normalize the shortwave and longwave fluxes to reduce their dependence from the solar angle and surface temperature respectively. Finally, we train the model with an additional heating rates penalty in the loss function.

Seasonal forecasts are commonly issued in the form of anomalies as departures from average in a specified multiyear reference period (climatology). The model climatology is estimated as average of retrospective forecasts over the hindcast period. However, different operational centers providing seasonal ensemble predictions use different hindcast periods based on their own model climatology. In addition, the hindcast period of recently developed/upgraded newer models tends to shift to the recent years. In this paper, we discuss recent challenges faced by the APCC multi-model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various sensitivity experiments for the MME prediction, we proposed to change the hindcast period that is the most appropriate solution for the APCC operations. It makes the newly developed models join the MME and increase the total number of participating models, which facilitates the skill improvement of the MME prediction.

Atmospheric gravity waves are known to be the source of medium-scale traveling ionospheric disturbances (MSTIDs) in the upper atmosphere. In recent studies, these gravity waves have mostly been linked to weather convection activities from tropospheric altitudes during the daytime. In this research work, we study the generation and dynamics of daytime MSTIDs induced by tropospheric convections over the Brazilian sector. Both observational and theoretical tools are employed to pursue these objectives. Data from space and ground-based instruments such as a network of GNSS receivers, digisonde, and meteorological satellites (GOES Satellite) are analyzed to identify the driving source of AGW-MSTIDs. The convectional-Atmosphere-Ionosphere-Coupled model (CAI-CM) is adapted to incorporate the dynamics of convectively generated AGWs and their coupling to the ionosphere. The model is used to analyze the source of AGW as they propagate from the lower atmosphere to the upper atmosphere and how MSTIDs are dependent on the sources that generate them.

Based on wintertime observations during the MOSAiC expedition in 2019-2020, it was found that Arctic cloud properties show significant differences when clouds are coupled to the fluxes of water vapor transport coming from upwind regions of sea ice leads.  Among these differences are that cloud liquid water path is considerably increased as  a function of lead fraction for observations of lead fraction above 0.02, whereas ice water path only shows some moderate level of dependency on lead fraction when deep precipitating clouds are considered. Cloud macro-physical properties like cloud base height and cloud thickness were found to be lower and thicker, respectively, for clouds coupled to the water vapor transport.To substantiate the findings from the MOSAiC data set, long-term measurements (2012-2022) at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiagvik, Alaska  are being used to study the climatology of clouds and their properties coupled to the sea ice concentration in the Western Arctic. The same methodology used for the MOSAiC study is feasible to be applied to the NSA ARM site thanks to the standard instrumentation dataset provided by the ARM program. The study focuses on the atmospheric boundary layer  topped  water vapor transport as mechanism to link  the influence of sea ice  leads or polynyas, to the clouds. Statistical results will be presented and set into context to the results found for the MOSAiC expedition.BERLING 2023 IUGG 28th GENERAL ASSEMBLY,  SESSION: M22d Cloud and Precipitation Studies, Convener: Greg McFarquhar (USA)

Attributing sources of tropical cyclogenesis (TCG) bias to large-scale circulation in global circulation models is challenging. Here, we propose the use of empirical orthogonal functions as an approach to understand model bias of TCG. Two leading modes of large-scale wind circulations in the West Pacific can explain the TCG frequency and location in both climate reanalysis and the MetUM model. In the reanalysis, the two modes distinguish the summer monsoon trough position and the strength of the north Pacific subtropical high. However, in the model, the wind circulations are biased towards the positive phase of simulated modes thus overestimating TCG in the entire Main Development Region. This bias is further related to the north-eastward shifted monsoon trough and a weakened subtropical high, and overly strong tropics-subtropics connections. This approach could be deployed more widely to other basins and models to diagnose the causes of TCG bias.

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of extreme winter weather in California, including ARs, SAWs and temperature extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1-4 lead. This methodology (i) uses dynamical model information considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters for dynamical model error/uncertainty at longer lead times, and (iii) increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.

Analog methods (AMs) have long been used for precipitation prediction and climate studies. However, they rely on manual selections of parameters, such as the predictor variables and analogy criterion. Previous work showed the potential of genetic algorithms (GAs) to optimize most parameters of AMs. This research goes one step further and investigates the potential of GAs for automating the selection of the input variables and the analogy criteria (distance metric between two data fields) in AMs. Our study focuses on daily precipitation prediction in central Europe, specifically Switzerland, as a representative case. Comparative analysis against established reference methods demonstrates the superiority of the GA-optimized AM in terms of predictive accuracy. The selected input variables exhibit strong associations with key meteorological processes that influence precipitation generation. Further, we identify a new analogy criterion inspired by the Teweles-Wobus criterion, but applied directly to grid values, which consistently performs better than other Euclidean distances. It shows potential for further exploration regarding its unique characteristics. In contrast to conventional stepwise selection approaches, the GA-optimized AM displays a preference for a flatter structure, characterized by a single level of analogy and an increased number of variables. Although the GA optimization process is computationally intensive, we highlight the use of GPU-based computations to significantly reduce computation time. Overall, our study demonstrates the successful application of GAs in automating input variable selection for AMs, with potential implications for application in diverse locations and data exploration for predicting alternative predictands.

Winter Euro-Atlantic atmospheric blocking events have significant socioeconomical impacts as they cause various types of weather extremes in a range of regions. According to current climate projections, fewer of these blocking events will occur as temperatures rise. However, the timing of such a reduction is currently highly uncertain. Meanwhile, recent studies indicate that using climate models with high enough ocean resolutions to simulate mesoscale eddies improve simulated winter Euro-Atlantic blocking events significantly. In this paper, we show from a large ensemble of climate simulations based on the highest emission scenario that largely prominent and coarsely resolved non-eddying climate models project a noticeable significant decline in blocking frequencies from the 2030s-2040s, whereas blocking statistics in eddy-permitting simulations are noticeably decreasing only from years 2060s. Our result suggests with a strong level of confidence that winter blocking activity over the next several decades will keep being dominated by internal variability.

NOAA Climate Prediction Center (CPC) has generated a 100-member ensemble of Atmospheric Model Intercomparison Project (AMIP) simulations from 1979 to present using the GFSv15 with FV3 dynamical core. The intent of this study is to document a development in an infrastructure capability with a focus to demonstrate the quality of these new simulations is on par with the previous GFSv2 AMIP simulations. These simulations are part of CPC’s efforts to attribute observed seasonal climate variability to SST forcings and get updated once a month by available observed SST. The performance of these simulations in replicating observed climate variability and trends, together with an assessment of climate predictability and the attribution of some climate events is documented. A particular focus of the analysis is on the US climate trend, Northern Hemisphere winter height variability, US climate response to three strong El Niño events, the analysis of signal to noise ratio (SNR), the anomaly correlation for seasonal climate anomalies, and the South Asian flooding of 2022 summer, and thereby samples wide aspects that are important for attributing climate variability. Results indicate that the new model can realistically reproduce observed climate variability and trends as well as extreme events, better capturing the US climate response to extreme El Niño events and the 2022 summer South Asian record-breaking flooding than GFSv2. The new model also shows an improvement in the wintertime simulation skill of US surface climate, mainly confined in the Northern and Southeastern US for precipitation and in the east for temperature.