Ensemble Members Research Articles

Diagnosing Factors Leading to an Incorrect Supercell Thunderstorm Forecast

Abstract On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models.

Multiweek tropical cyclone prediction for the Southern Hemisphere in ACCESS‐S2: Maintaining operational skill and continuity of service

AbstractThe skill of subseasonal (multiweek) forecasts of tropical cyclone (TC) occurrence over the Southern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS‐S2. Relative to its predecessor, ACCESS‐S1, ACCESS‐S2 shows improved biases in spatial TC frequency in the South Pacific and Southwest Indian Ocean. However, there is no improvement to the known negative bias in TC frequency off the coast of NW Australia. The ability of ACCESS‐S2 to provide probabilistic forecasts of TC occurrence for the Southern Hemisphere on multiweek timescales is examined using reliability measures and Brier skill scores. For the period November–February 1990–2012, both ACCESS‐S1 and ACCESS‐S2 show positive skill relative to climatology for calibrated forecasts out to week 5. However, the skill of ACCESS‐S2 is slightly reduced compared to ACCESS‐S1 at all lead times, which may be due to the fewer number of ensemble members available. For the full ACCESS‐S2 hindcast period, November–April 1981–2018, ACCESS‐S2 again shows positive skill of calibrated forecasts over climatology out to week 5. For weeks 1–2, skill is reduced compared to the shorter 1990–2012 period; whereas it is marginally improved for longer lead times (weeks 3–5). Use of lagged ensembles, an alternative linear regression calibration, as well as removing weaker model TCs were examined to potentially improve the skill of ACCESS‐S2 forecasts; however, none of these methods were able to significantly increase skill at all lead times. Continued use of the original calibration method is therefore recommended in order to retain skill and continuity of service of the BoM operational and public multiweek TC forecasts.

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

Error homogenization in physics-informed neural networks for modeling in manufacturing

Physics-informed neural networks (PINNs) have demonstrated effectiveness in solving partial differential equations (PDEs) associated with manufacturing scenarios, due to their physically interpretable training logic. One limitation has been that PINNs often exhibit heterogenous error maps and high-error “hot spots” throughout the solution domain, which reduce not only the solutions’ accuracy but also their overall consistency with the physical laws. This study addresses this limitation by presenting an efficient and error-aware PINN ensembling technique for error homogenization in solving manufacturing problems. Specifically, a PINN is first established by constraining its training process through a manufacturing specific PDE and corresponding boundary conditions to ensure physical consistency. Next, the loss landscape in the neighborhood of three PINNs trained with varying network parameter initialization is sampled to generate a PINN ensemble. Finally, the outputs of the ensemble members are combined through an inverse error-weighted average to yield the prediction of the PDE solution. Evaluation using the Allen-Cahn PDE, which describes phase separation in the solidification of metallic alloys, shows that the developed method reduces the average prediction error by 63% and error standard deviation by 30% across the solution space, demonstrating its effectiveness for PINN error reduction and homogenization. Additionally, the method has also demonstrated 96% reduction in the computational time as compared to conventional ensembling methods.

A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand

A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.

Comment on “Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5‐T2m” by N. Scafetta (2022)

AbstractScafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that “all models with ECS > 3.0°C overestimate the observed global surface warming” is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion “that spatial t‐statistics rejects the data‐model agreement” is also not sustainable.

Reply to “Comment on ‘Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5‐T2m’ by N. Scafetta (2022)” by Schmidt et al. (2023)

AbstractSchmidt, Jones, and Kennedy’s (SJK) (2023, https://doi.org/10.1029/2022GL102530) critique of Scafetta (2022, https://doi.org/10.1029/2022GL097716) is flawed. Their assessment of the error of the ERA‐T2m 2011–2021 mean (≈0.10°C) is 5–10 times overestimated and contradicts published literature. SJK confused natural variability with random noise and mistook the error of the mean of a temperature chronology for the stochastic error of the regression parameter M of a nonphysical isothermal climate model (T(t) = M). SJK's allegations regarding the internal variability of the models, the role of the global climate model ensemble members, and other issues were partially addressed in Scafetta (2022, https://doi.org/10.1029/2022GL097716) and, later, more extensively in Scafetta (2023a, https://doi.org/10.1007/s00382-022-06493-w) where Scafetta's (2022, https://doi.org/10.1029/2022GL097716) conclusions were confirmed.

A review of ENSO teleconnections at present and under future global warming

AbstractThe El Niño‐Southern Oscillation (ENSO) is a major component of the Earth's climate that largely influences global climate variability through long‐distance teleconnections. Rossby wave trains emerging from the tropical convection and their propagation into extratropical regions are the key mechanism for tropical and extratropical teleconnections. Despite significant progress in the understanding of ENSO teleconnections over the recent past decades, several important issues have remained to be addressed. The global atmospheric teleconnections of ENSO vary substantially with the seasonal cycle, on the decadal timescale, and under the influence of global warming. It is essential to separate the internal decadal variability of ENSO teleconnections from changes caused by the external forcing of global warming. However, the post‐satellite observations are not long enough to compose a large number of ENSO events to distinguish the decadal variability of ENSO teleconnections from changes related to increasing greenhouse concentrations. The current climate models also suffer from common biases, such that they are unable to properly reproduce both the tropical mean state and some features of ENSO. Nevertheless, observational records can be extended back in time via reconstruction methods. Efforts have also already been made to remove some main common biases of climate models and to improve the representation of ENSO characteristics. The reliable reconstructed data along with a large number of ensemble members of the improved climate model simulations can be applied to advance our understanding of ENSO global teleconnections and their responses to internal decadal variability and externally forced global warming.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Climate Models and Modeling > Earth System Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

Stochastic Bifurcation of the North Atlantic Circulation under a Midrange Future Climate Scenario with the NASA-GISS ModelE

Abstract A 10-member ensemble simulation with the NASA GISS-E2-1-G climate model shows a clear bifurcation in the Atlantic meridional overturning circulation (AMOC) strength under the SSP2–4.5 extended scenario. At 26°N, the bifurcation leads to 8 strong AMOC and 2 much weaker AMOC states, while at 48°N, it leads to 8 stable AMOC-on and 2 nearly AMOC-off states, the latter lasting approximately 800 years. A variety of fully coupled models have demonstrated tipping points in AMOC through hosing experiments, i.e., prescribing sufficient freshwater inputs in the subpolar North Atlantic. In the GISS simulations, there are no external freshwater perturbations. The bifurcation arises freely in the coupled system and is the result of stochastic variability (noise-induced bifurcation) associated with sea ice transport and melting in the Irminger Sea after a slowing of the greenhouse gas forcing. While the AMOC strength follows the near shutdown of the Labrador Sea deep convection initially, the Irminger Sea salinity and deep mixing determine the timing of the AMOC recovery or near collapse at 48°N, which varies widely across the ensemble members. Other feedbacks such as ice-albedo, ice-evaporation, E − P, and the overturning salt-advection feedback play a secondary role that may enhance or reduce the primary mechanism which is ice melt. We believe this is the first time that a coupled climate model has shown such a bifurcation across an initial condition ensemble and might imply that there is a chance for significant and prolonged AMOC slow down due to internal variability of the system. Significance Statement We believe this is the first time that divergent AMOC behavior has been reported for an ensemble of Earth system model simulations using identical climate forcing and no prescribed freshwater perturbations. This response is a manifestation of noise-induced bifurcation, enhanced by feedbacks, revealing the role stochastic (or intrinsic) variability may play in AMOC stability.

Assessing the Ensemble Assimilation System for Forecasting the Genesis of Tropical Cyclone Sonca (2017) in the Vietnam East Sea using a Lagrangian Approach

: With the aim of evaluating the cyclogenesis predictability of Sonca (2017) using an ensemble prediction system from the local ensemble transform Kalman filter (LETKF) to model WRF, the study proposed a dynamical assessment method with a Lagrangian approach that is stable and independent of time and reference systes. With this approach, the study can clearly distinguish between cases of tropical cyclone (TC) formation and non-formation in the ensemble forecast members. By evaluating the thermodynamic conditions of the low-level circulation between the forming and non-forming groups, the study found that the ocean-atmosphere interaction and the vortex merger process play imnportant roles in the formation of Sonca. The evaluation of the formation location showed that the detected vortex centers from the forming members tended to concentrate near the monsoon trough axis, indicating that this is a highly favorable region for vortex formation in the Vietnam East Sea.
 
 

A critical evaluation of decadal solar cycle imprints in the MiKlip historical ensemble simulations

Abstract. Studies concerning solar–terrestrial connections over the last decades claim to have found evidence that the quasi-decadal solar cycle can have an influence on the dynamics in the middle atmosphere in the Northern Hemisphere (NH) during the winter season. It has been argued that feedbacks between the intensity of the UV part of the solar spectrum and low-latitude stratospheric ozone may produce anomalies in meridional temperature gradients which have the potential to alter the zonal-mean flow in middle to high latitudes. Interactions between the zonal wind and planetary waves can lead to a downward propagation of the anomalies, produced in the middle atmosphere, down to the troposphere. More recently, it has been proposed that top-down-initiated decadal solar signals might modulate surface climate and synchronize the North Atlantic Oscillation. A realistic representation of the solar cycle in climate models was suggested to significantly enhance decadal prediction skill. These conclusions have been debated controversial since then due to the lack of realistic decadal prediction model setups and more extensive analysis. In this paper we aim for an objective and improved evaluation of possible solar imprints from the middle atmosphere to the surface and with that from head to toe. Thus, we analyze model output from historical ensemble simulations conducted with the state-of-the-art Max Planck Institute for Meteorology Earth System Model in high-resolution configuration (MPI-ESM-HR). The target of these simulations was to isolate the most crucial model physics to foster basic research on decadal climate prediction and to develop an operational ensemble decadal prediction system within the “Mittelfristige Klimaprognose” (MiKlip) framework. Based on correlations and multiple linear regression analysis we show that the MPI-ESM-HR simulates a realistic, statistically significant and robust shortwave heating rate and temperature response at the tropical stratopause, in good agreement with existing studies. However, the dynamical response to this initial radiative signal in the NH during the boreal winter season is weak. We find a slight strengthening of the polar vortex in midwinter during solar maximum conditions in the ensemble mean, which is consistent with the so-called “top-down” mechanism. The individual ensemble members, however, show a large spread in the dynamical response with opposite signs in response to the solar cycle, which might be a result of the large overall internal variability compensating for rather small solar imprints. We also analyze the possible surface responses to the 11-year solar cycle and review the proposed synchronization between the solar forcing and the North Atlantic Oscillation. We find that the simulated westerly wind anomalies in the lower troposphere, as well as the anomalies in the mean sea level pressure, are most likely independent from the timing of the solar signal in the middle atmosphere and the alleged top-down influences. The pattern rather reflects the decadal internal variability in the troposphere, mimicking positive and negative phases of the Arctic and North Atlantic oscillations throughout the year sporadically, which is then assigned to the solar predictor time series without any plausible physical connection and sound solar contribution. Finally, by applying lead–lag correlations, we find that the proposed synchronization between the solar cycle and the decadal component of the North Atlantic Oscillation might rather be a statistical artifact, affected for example by the internal decadal variability in the ocean, than a plausible physical connection between the UV solar forcing and quasi-decadal variations in the troposphere.

Reliability of simulating internal precipitation variability over multi‐timescales using multiple global climate model large ensembles in China

AbstractSingle model initial‐condition large ensembles (SMILEs) are valuable means to study the role of internal climate variability (ICV) in climate change. However, the ability of SMILEs to represent the multi‐timescale ICV is not fully understood, especially in the region of China. The main objective of this work is to assess the ability of six advanced SMILEs with ensemble members ranging from 30 to 100 in simulating ICV of precipitation at various timescales in China, using a set of observational datasets as the benchmark. To have a first insight into the selected models, the mean states and trends of annual precipitation were evaluated at the very beginning. The ICV of precipitation was then calculated at multiple timescales by two widely used methods of representing temporal variability and intermember variability. The minimum required ensemble size for each climate model to robustly capture the true value of ICV is investigated at last. The results show that the multiple SMILEs can capture the basic spatial distribution pattern of ICV across timescales. However, the ICV represented by temporal variability underestimates observed ICV in most regions across timescales. The ICV represented by intermember variability is similar to that represented by temporal variability at the interannual and interdecadal timescales, but significantly larger at the multidecadal timescales. Within ±10% error limit, 20–30 members are sufficient for all climate models at the interannual timescale while ensemble sizes of 74% or more of the full sizes for each climate model are needed at multidecadal timescales.

A Novel Response Priority Framework for an Urban Coastal Catchment Using Global Weather Forecasts‐Based Improved Flood Risk Estimates

AbstractA novel flood risk forecasting‐based response priority framework is proposed in this study, which will facilitate efficient decision‐making ahead of an extreme precipitation event. This study is demonstrated over a highly flood‐prone and geomorphologically diverse coastal catchment in Mumbai city, the financial capital of India, and is subjected to high spatio‐temporal variability of precipitation. A regional precipitation forecast model is developed, where a quantile regression (QR)‐based statistical approach is applied to the global weather forecasts for improvement in finer resolution extreme precipitation estimates. A QR is performed between the observed synoptic‐scale predictors obtained from ERA‐Interim Reanalysis data set and the observed subdaily precipitation within the study area. Subsequently, a set of reliably well‐simulated forecasted predictors obtained from the ensemble members of the Global Ensemble Forecast System are used in the established relationship to obtain extreme precipitation forecasts for higher quantile levels. The forecasted precipitation data serves as an essential input to a comprehensive hydrodynamic flood modeling framework. The flood inundation maps corresponding to different quantiles of forecasted precipitation are quantified to identify the flood hotspots. The set of flood inundations maps developed for different quantiles are utilized to demarcate the areas based on critical response time, that is, the time required by each cell within the study area to be inundated to a certain threshold depth, and the response priority levels for each cell are determined. This information is combined with socioeconomic vulnerability (SEV), a critical component of flood risk, to derive conjugated SEV‐based response priority maps.

On the predictability of a heavy rainfall event with dual rainbands

An extremely heavy rainfall with dual rainbands occurred in the Fujian province on 22 May 2014, causing severe disasters in Fujian. In order to investigate the key forecasting factors and the predictability of this case, the evaluation and sensitivity analysis of precipitation forecasts by the ensemble prediction system which is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) global Model are carried out. The result show that the ECMWF-EPS have better ability to capture the intensity and spatial distribution of the northern rainband, but significantly underestimated the precipitation in the warm area. Through ensemble forecast sensitivity analysis and comparison between good and poor members, the main factors causing forecast deviations in the two rainbands and affecting the predictability of heavy rainfall were revealed. The forecast of rainfall distribution and intensity in the northern rainband was highly sensitive to the predictability of the weather-scale shear line. The westward bias in the forecasted position and the weakened intensity of the shear line were the main causes of the westward and weaker forecast of heavy precipitation in the northern region. Additionally, the forecast of 850 hPa low-level jets, especially the forecasted intensity of zonal winds, which were closely related to the shear line, significantly influenced the intensity forecast of precipitation in the northern region. The forecast of precipitation in the southern warm area, which was far from the shear line, was more sensitive to the wind speed and thermodynamic conditions of the southwestern airflow in the boundary layer of the upstream inflow region. Most ensemble forecast members underestimated the intensity of the southwestern airflow in the coastal boundary layer of South China, which was the main reason for the near omission of heavy rainfall in the southern warm area. This study provides a quantitative correlation between low-level southwest jets along the coast of Guangdong and Fujian and non-typhoon heavy rainfall in Fujian, and explores their impact on heavy precipitation forecasts.

Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment.

The occurrence and severity of extreme precipitation events have been increasing globally. Although numerous projections have been proposed and developed for evaluating the climate change impacts, most models suffer from significant bias error due to the coarse resolution of the climate datasets, which affects the accuracy of the climate change assessment. Therefore, in this study, post-processing techniques (interpolation and bias correction methods) were adopted on the database for Policy Decision Making for Future Climate Change (d4PDF) model for extreme climatic flood events simulation in the Chao Phraya River Basin, Thailand, under + 4-K future climate simulation. Due to the limited number of the rain gages, the gradient plus inverse distance squared interpolation method (combination of multiple linear regression and distance weighting methods) was applied in this study. In the bias correction methods, the additional setting of monthly and seasonal periods was adjusted. The proposed bias correction approach deployed gamma distribution combined with generalized Pareto distribution setting with the seasonal period for the rainy season datasets, whereas only the gamma setting was applied with the monthly period during the dry season. The outcomes revealed that the proposed method could react to extreme rainfall events, expand dry days during dry season, and intensify rainfall amount during rainy season. The post-processing d4PDF trends of six sea surface temperature (SST) patterns (consists of 90 ensemble members) of two periods (near future: 2051-2070 and far future: 2091-2110) recorded the highest and lowest amounts of annual rainfalls of 4,450 mm/year in mid-stream of Nan River and 710 mm/year in the lower CPRB, respectively. Notably, the significant variances noted in the rainfall patterns among ensembles, demanding further investigation in future climate change, impact studies. The findings of the study provided novel insights on the importance of proper post-processing techniques for improving the robustness of d4PDF in climate change impacts assessment.

Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections

AbstractThe Caspian Sea level (CSL) is strongly sensitive to climate‐induced changes in its water balance components, especially evaporation from its surface as the main expenditure component of the Caspian Sea's (CS) water budget. Projecting evaporation and determining associated uncertainties obtained from such studies are critical for reliably predicting future CSL fluctuations and developing mitigation and adaptation strategies. This paper studies the projected changes in evaporation from the CS using 18 global climate models (GCMs) from the latest Coupled Model Intercomparison Project phase 6 (CMIP6) and Meyer's semi‐empirical formula. Future evaporation projections are constructed employing a weighted combination of the top‐ranked GCMs, including all ensemble members of selected individual models in a probabilistic framework. This study estimates late 21st century median evaporation of 945, 1016, 1105 and 1173 mm under the low‐emission, medium‐emission, medium‐to‐high emission and high‐emission scenarios, respectively. The weighted multimodel ensemble suggests CS's annual mean evaporation is projected to substantially increase by 3.9%, 13.2%, 20% and 27.9% under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, respectively, for the late 21st century against the reference period. According to the spatial distribution of evaporation, arid eastern regions of CS experience higher evaporation than semi‐arid western and southwestern temperate regions. Additionally, the northwestern continental regions experience the least evaporation over the CS.

Assessment of the Pan-Arctic Accelerated Rate of Sea Ice Decline in CMIP6 Historical Simulations

Abstract Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.

Bifurcation Points for Tropical Cyclone Genesis and Intensification in Sheared and Dry Environments

Abstract The combination of moderate vertical wind shear (VWS) and dry environments can produce the most uncertain scenarios for tropical cyclone (TC) genesis and intensification. We investigated the sources of increased uncertainty of TC development under moderate VWS and dry environments using a set of Weather Research and Forecasting (WRF) ensemble simulations. Statistical analysis of ensemble members for precursor events and time-lagged correlations indicates that successful TC development is dependent on a specific set of precursor events. A deficiency in any of these precursor events leads to a failure of TC intensification. The uncertainty of TC intensification can be largely attributed to the probabilistic characteristics of precursor events lining up together before TC intensification. The critical bifurcation point between successful and failed trials in these idealized simulations is the sustained vortex alignment process. Even for the failed intensification cases, most simulations showed deep organized convection, which reformed a midlevel vortex. However, for the failed cycles, the new midlevel vortex could not sustain vertical alignment with the low-level center and was carried away by VWS shortly. Under the most uncertain setup (VWS = 7.5 m s−1 and 50% moisture), the latest-developing ensemble member had seven events of tilt decreasing and increasing again that occurred during the 8 days before genesis. Some unsuccessful precursor events looked very close to the successful ones, implying limits on the intrinsic predictability for TC genesis and intensification in moderately sheared and dry environments. Significance Statement The aim of this study is to identify a critical bifurcation point that determines whether tropical disturbances in moderately sheared and dry environments will develop into intense storms or dissipate. When it comes to predicting the formation and strength of tropical cyclones, vertical wind shear, where the environmental wind changes with height, presents a challenging scenario. When the shear is neither too weak nor too strong, some systems manage to develop into cyclones, while others get torn apart under similar shear conditions. Understanding the differences between these outcomes remains a puzzle. Through extensive computer simulations, we have discovered a key factor that contributes to the uncertainty surrounding the alignment of the midlevel vortex with the center of the low-level vortex. These results reveal the complexity and multiple sources of uncertainty involved in forecasting tropical cyclone intensification, providing valuable insights into why moderate shear is a particularly challenging regime to predict tropical genesis and intensification.

Kilometer-Scale Multimodel and Multiphysics Ensemble Simulations of a Mesoscale Convective System in the Lee of the Tibetan Plateau: Implications for Climate Simulations

Abstract Kilometer-scale climate model simulations are useful tools to investigate past and future changes in extreme precipitation, particularly in mountain regions, where convection is influenced by complex topography and land–atmosphere interactions. In this study, we evaluate simulations of a flood-producing mesoscale convective system (MCS) downstream of the Tibetan Plateau (TP) in the Sichuan basin from a kilometer-scale multimodel and multiphysics ensemble. The aim is to better understand the physical processes that need to be correctly simulated for successfully capturing downstream MCS formation. We assess how the ensemble members simulate these processes and how sensitive the simulations are to different model configurations. The preceding vortex evolution over the TP, its interaction with the jet stream, and water vapor advection into the basin are identified as key processes for the MCS formation. Most modeling systems struggle to capture the interaction between the vortex and jet stream, and perturbing the model physics has little impact, while constraining the large-scale flow by spectral nudging improves the simulation. This suggests that an accurate representation of the large-scale forcing is crucial to correctly simulate the MCS and associated precipitation. To verify whether the identified shortcomings systematically affect the MCS climatology in longer-term simulations, we evaluate a 1-yr WRF simulation and find that the seasonal cycle and spatial distribution of MCSs are reasonably well captured and not improved by spectral nudging. While the simulations of the MCS case highlight challenges in extreme precipitation forecasting, we conclude that these challenges do not systematically affect simulated climatological MCS characteristics. Significance Statement Convective storm systems in mountain regions are not well understood, because the spatial resolution in conventional regional climate models is too coarse to resolve relevant processes. Here, we evaluate high-resolution climate model simulations of a storm system on the downwind side of the Tibetan Plateau. Understanding which models and model setups work well to represent this type of storm system is important because high-resolution models can help us understand mechanisms of storm formation in mountain regions and how climate change will affect these. A key finding is that most of the models struggle to capture the selected storm case, while a 1-yr simulation shows that the general statistics of storm systems around the Tibetan Plateau are still reasonably well captured.

Spatial-Mode-Based Calibration (SMoC) of Forecast Precipitation Fields with Spatially Correlated Structures: An Extended Evaluation and Comparison with Gridcell-by-Gridcell Postprocessing

Abstract Postprocessing forecast precipitation fields from numerical weather prediction models aims to produce ensemble forecasts that are of high quality at each grid cell and, importantly, are spatially structured in an appropriate manner. A conventional approach, the gridcell-by-gridcell postprocessing, typically consists of two steps: 1) perform statistical calibration separately at individual grid cells to generate unbiased, skillful, and reliable ensemble forecasts and 2) employ ensemble reordering to link ensemble members of all grid cells according to certain templates to form spatially structured ensemble forecasts. However, ensemble reordering techniques are generally problematic in practical use. For example, the well-known Schaake shuffle is often criticized for not considering real physical atmospheric conditions. In this context, a fundamentally new approach, namely, spatial-mode-based calibration (SMoC), has recently been developed for postprocessing forecast precipitation fields with inbuilt spatial structures, thereby eliminating the need for ensemble reordering. SMoC was tested on 1-day-ahead forecasts of heavy precipitation events and was found to produce ensemble forecasts with appropriate spatial structures. In this paper, we extend SMoC to calibrate forecasts of light and no precipitation events and forecasts at long lead times. We also compare SMoC with the gridcell-by-gridcell postprocessing. Results based on multiple evaluation metrics show that SMoC performs well in calibrating both forecasts of light and no precipitation events and forecasts at long lead times. Compared with the gridcell-by-gridcell postprocessing, SMoC produces ensemble forecasts with similar forecast skill, improved forecast reliability, and clearly better spatial structures. In addition, SMoC is computationally far more efficient.