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Zili Shen, Anmin Duan, Dongliang Li, and Jinxiao Li

Abstract

The capability of 36 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and their 24 CMIP5 counterparts in simulating the mean state and variability of Arctic sea ice cover for the period 1979–2014 is evaluated. In addition, a sea ice cover performance score for each CMIP5 and CMIP6 model is provided that can be used to reduce the spread in sea ice projections through applying weighted averages based on the ability of models to reproduce the historical sea ice state. Results show that the seasonal cycle of the Arctic sea ice extent (SIE) in the multimodel ensemble (MME) mean of the CMIP6 simulations agrees well with observations, with a MME mean error of less than 15% in any given month relative to the observations. CMIP6 has a smaller intermodel spread in climatological SIE values during summer months than its CMIP5 counterpart. In terms of the monthly SIE trends, the CMIP6 MME mean shows a substantial reduction in the positive bias relative to the observations compared with that of CMIP5. The spread of September SIE trends is very large, not only across different models but also across different ensemble members of the same model, indicating a strong influence of internal variability on SIE evolution. Based on the assumptions that the simulations of CMIP6 models are from the same distribution and that models have no bias in response to external forcing, we can infer that internal variability contributes to approximately 22% ± 5% of the September SIE trend over the period 1979–2014.

Open access
Lauriana C. Gaudet, Kara J. Sulia, Tzu-Chin Tsai, Jen-Ping Chen, and Jessica P. Blair

Abstract

Microphysical processes within mixed-phase convective clouds can have cascading impacts on cloud properties and resultant precipitation. This paper investigates the role of microphysics in the lake-effect storm (LES) observed during intensive observing period 4 of the Ontario Winter Lake-effect Systems field campaign. A microphysical ensemble is composed of 24 simulations that differ in the microphysics scheme used (e.g., Weather Research and Forecasting Model microphysics options or a choice of two bulk adaptive habit models) along with changes in the representation of aerosol and potential ice nuclei concentrations, ice nucleation parameterizations, rain and ice fall speeds, spectral indices, ice habit assumptions, and the number of moments used for modeling ice-phase hydrometeors in each adaptive habit model. Each of these changes to microphysics resulted in varied precipitation types at the surface; 15 members forecast a mixture of snow, ice, and graupel, 7 members forecast only snow and ice, and the remaining 2 members forecast a combination of snow, ice, graupel, and rain. Observations from an optical disdrometer positioned to the south of the LES core indicate that 92% of the observed particles were snow and ice, 5% were graupel, and 3% were rain and drizzle. Analysis of observations spanning more than a point location, such as polarimetric radar observations and aircraft measurements of liquid water content, provides insight into cloud composition and processes leading to the differences at the surface. Ensemble spread is controlled by hydrometeor type differences spurred by processes or parameters (e.g., ice fall speed) that affect graupel mass.

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Anders A. Jensen, James O. Pinto, Sean C. C. Bailey, Ryan A. Sobash, Gijs de Boer, Adam L. Houston, Phillip B. Chilson, Tyler Bell, Glen Romine, Suzanne W. Smith, Dale A. Lawrence, Cory Dixon, Julie K. Lundquist, Jamey D. Jacob, Jack Elston, Sean Waugh, and Matthias Steiner

Abstract

Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

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Wei Sun and Youping Xu

Abstract

A detailed investigation about the effects of the Microwave Humidity Sounder-2 (MWHS-2) radiances on board the Fengyun-3D (FY-3D) satellite is combined with developments within the Weather Research and Forecasting Data Assimilation (WRFDA) system and analyses on the evolution of the heavy rainfall associated with Typhoon Ampil during 23–24 July 2018. In the analysis field, the position of Typhoon Ampil is found out to be distinctly affected by the MWHS-2 assimilation. The experiment that assimilates MWHS-2 radiances through hybrid-3DVAR generates the best analysis with large increments around the typhoon, which contributes to the typhoon moving inland to the southwest. In the forecast fields, the MWHS-2 assimilation improves the rainfall in not only the accumulated amount, but also the evolution characteristics. The hybrid-3DVAR experiment reduces the RMSE of the rainfall amount, and enhances the spatial correlation and the fractions skill score of the rainfall evolution to the greatest extent, followed by the 3DVAR MWHS-2 experiment. As for the cause of the rainfall improvements, analyses suggest that it could be closely connected with the characteristics of the circulation structures related to the typhoon evolution. On one hand, the increases of the rainfall amount and intensities in the MWHS-2 assimilation experiments (previously underestimated) correspond to the strengthened typhoon structures with strong anomalies in both the upper-layer temperature and the lower-layer geopotential height. On the other hand, the better rainfall evolution in the hybrid-3DVAR experiment could be explained by its clearer evolution of the structure of typhoon under the effects of an approaching upper trough, and its smallest typhoon track errors around the middle time period.

Open access
David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

Abstract

This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.

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John T. Allen, Edwina R. Allen, Harald Richter, and Chiara Lepore

Abstract

During 2013, multiple tornadoes occurred across Australia, leading to 147 injuries and considerable damage. This prompted speculation as to the frequency of these events in Australia, and whether 2013 constituted a record year. Leveraging media reports, public accounts, and the Bureau of Meteorology observational record, 69 tornadoes were identified for the year in comparison to the official count of 37 events. This identified set and the existing historical record were used to establish that, in terms of spatial distribution, 2013 was not abnormal relative to the existing climatology, but numerically exceeded any year in the bureau’s record. Evaluation of the environments in which these tornadoes formed illustrated that these conditions included tornado environments found elsewhere globally, but generally had a stronger dependence on shear magnitude than direction, and lower lifting condensation levels. Relative to local environment climatology, 2013 was also not anomalous. These results illustrate a range of tornadoes associated with cool season, tropical cyclone, east coast low, supercell tornado, and low shear/storm merger environments. Using this baseline, the spatial climatology from 1980 to 2019 as derived from the nonconditional frequency of favorable significant tornado parameter environments for the year is used to highlight that observations are likely an underestimation. Applying the results, discussion is made of the need to expand observing practices, climatology, forecasting guidelines for operational prediction, and improve the warning system. This highlights a need to ensure that the general public is appropriately informed of the tornado hazard in Australia, and provide them with the understanding to respond accordingly.

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Xiaomin Chen, Jian-Feng Gu, Jun A. Zhang, Frank D. Marks, Robert F. Rogers, and Joseph J. Cione

Abstract

This study investigates the precipitation symmetrization preceding rapid intensification (RI) of tropical cyclones (TCs) experiencing vertical wind shear by analyzing numerical simulations of Typhoon Mujigae (2015) with warm (CTL) and relatively cool (S1) sea surface temperatures (SSTs). A novel finding is that precipitation symmetrization is maintained by the continuous development of deep convection along the inward flank of a convective precipitation shield (CPS), especially in the downwind part. Beneath the CPS, downdrafts flush the boundary layer with low-entropy parcels. These low-entropy parcels do not necessarily weaken the TCs; instead, they are “recycled” in the TC circulation, gradually recovered by positive enthalpy fluxes, and develop into convection during their propagation toward a downshear convergence zone. Along-trajectory vertical momentum budget analyses reveal the predominant role of buoyancy acceleration in the convective development in both experiments. The boundary layer recovery is more efficient for warmer SST, and the stronger buoyancy acceleration accounts for the higher probability of these parcels developing into deep convection in the downwind part of the CPS, which helps maintain the precipitation symmetrization in CTL. In contrast, less efficient boundary layer recovery and less upshear deep convection hinder the precipitation symmetrization in S1. These findings highlight the key role of boundary layer recovery in regulating the precipitation symmetrization and upshear deep convection, which further accounts for an earlier RI onset timing of the CTL TC. The inward-rebuilding pathway also illuminates why deep convection is preferentially located inside the radius of maximum wind of sheared TCs undergoing RI.

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Chao Li, Francis Zwiers, Xuebin Zhang, Guilong Li, Ying Sun, and Michael Wehner

Abstract

This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

Open access
Adam Vaccaro, Julien Emile-Geay, Dominque Guillot, Resherle Verna, Colin Morice, John Kennedy, and Bala Rajaratnam

Abstract

Surface temperature is a vital metric of Earth’s climate state but is incompletely observed in both space and time: over half of monthly values are missing from the widely used HadCRUT4.6 global surface temperature dataset. Here we apply the graphical expectation–maximization algorithm (GraphEM), a recently developed imputation method, to construct a spatially complete estimate of HadCRUT4.6 temperatures. GraphEM leverages Gaussian Markov random fields (also known as Gaussian graphical models) to better estimate covariance relationships within a climate field, detecting anisotropic features such as land–ocean contrasts, orography, ocean currents, and wave-propagation pathways. This detection leads to improved estimates of missing values compared to methods (such as kriging) that assume isotropic covariance relationships, as we show with real and synthetic data. This interpolated analysis of HadCRUT4.6 data is available as a 100-member ensemble, propagating information about sampling variability available from the original HadCRUT4.6 dataset. A comparison of Niño-3.4 and global mean monthly temperature series with published datasets reveals similarities and differences due in part to the spatial interpolation method. Notably, the GraphEM-completed HadCRUT4.6 global temperature displays a stronger early twenty-first-century warming trend than its uninterpolated counterpart, consistent with recent analyses using other datasets. Known events like the 1877/78 El Niño are recovered with greater fidelity than with kriging, and result in different assessments of changes in ENSO variability through time. Gaussian Markov random fields provide a more geophysically motivated way to impute missing values in climate fields, and the associated graph provides a powerful tool to analyze the structure of teleconnection patterns. We close with a discussion of wider applications of Markov random fields in climate science.

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Ju Liang, Jennifer L. Catto, Matthew Hawcroft, Kevin I. Hodges, Mou Leong Tan, and James M. Haywood

Abstract

Borneo vortices (BVs) are intense precipitating winter storms that develop over the equatorial South China Sea and strongly affect the weather and climate over the western Maritime Continent because of their association with deep convection and heavy rainfall. In this study, the ability of the Hadley Centre Global Environment Model 3–Global Coupled, version 3.1 (HadGEM3-GC3.1), global climate model to simulate the climatology of BVs at different horizontal resolutions is examined using an objective feature-tracking algorithm. The HadGEM3-GC3.1 at the N512 (25 km) horizontal resolution simulates BVs with well-represented characteristics, including their frequency, spatial distribution, and lower-tropospheric structures when compared with BVs identified in a climate reanalysis, whereas the BVs in the N96 (~135 km) and N216 (~65 km) simulations are much weaker and less frequent. Also, the N512 simulation better captures the contribution of BVs to the winter precipitation in Borneo and the Malay Peninsula when compared with precipitation from a reanalysis data and from observations, whereas the N96 and N216 simulations underestimate this contribution because of the overly weak low-level convergence of the simulated BVs. The N512 simulation also exhibits an improved ability to reproduce the modulation of BV activity by the occurrence of northeasterly cold surges and active phases of the Madden–Julian oscillation in the region, including increased BV track densities, intensities, and lifetimes. A sufficiently high model resolution is thus found to be important to realistically simulate the present-climate precipitation extremes associated with BVs and to study their possible changes in a warmer climate.

Open access