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Paul Flanagan and Rezaul Mahmood

Abstract

Extreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River basin (MoRB), covering an area over 1.29 million km2, is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features that impact the region on a seasonal and annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This situation warrants an analysis of extreme precipitation, especially relating to subannual variations when extreme precipitation is more impactful. To this end, data from 131 U.S. Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 to 2019. Annual 99th-percentile events and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th-percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increase in event frequency and intensity. Frequency shifts in the 99th-percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represents a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950–2019 period.

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Carolyne B. Machado, Thamiris L. O. B. Campos, Sameh A. Abou Rafee, Jorge A. Martins, Alice M. Grimm, and Edmilson D. de Freitas

Abstract

In this work, the trend of extreme rainfall indices in the macrometropolis of São Paulo (MMSP) was analyzed and correlated with large-scale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm yr−1 on average, according to a Mann–Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific decadal oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the temperature of South Atlantic (TSA), the AAO, El Niño–South Oscillation (ENSO), and the multidecadal oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R 2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the 1970s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than was the occurrence of dry months.

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Christian Philipp Lackner, Bart Geerts, and Yonggang Wang

Abstract

A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecasting Model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent-past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-yr period centered on 2050. A pseudo–global warming approach is used, with the driver reanalysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-yr retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snowpack Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain; 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes, making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations, changes in SWE are predicted to not be significant in the midseason. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas and decreases by 25.8% at the base elevations.

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Colin M. Zarzycki, Paul A. Ullrich, and Kevin A. Reed

Abstract

This article describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products with one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or for regional domains. Simple-to-visualize “scorecards” allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher-resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic Ocean TC frequency. Upstream refinement over northern Africa offers little benefit in simulating storm occurrence, but spatial genesis patterns are improved. We also show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on the version of the Morrison–Gettelman microphysics package that is used.

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Benjamin Pohl, Thomas Saucède, Vincent Favier, Julien Pergaud, Deborah Verfaillie, Jean-Pierre Féral, Ylber Krasniqi, and Yves Richard

Abstract

Daily weather regimes are defined around the Kerguelen Islands (Southern Ocean) on the basis of daily 500-hPa geopotential height anomalies derived from the ERA5 ensemble reanalysis over the period 1979–2018. Ten regimes are retained as significant. Their occurrences are highly consistent across reanalysis ensemble members. Regimes show weak seasonality and nonsignificant long-term trends in their occurrences. Their sequences are usually short (1–3 days), with extreme persistence values above 10 days. Seasonal regime frequency is mostly driven by the phase of the southern annular mode over Antarctica, midlatitude dynamics over the Southern Ocean such as the Pacific–South American mode, and, to a lesser extent, tropical variability, with significant but weaker relationships with El Niño–Southern Oscillation. At the local scale over the Kerguelen Islands, regimes have a strong influence on measured atmospheric and oceanic variables, including minimum and maximum air temperature, mostly driven by horizontal advections, seawater temperature recorded 5 m below the surface, wind speed, and sea level pressure. Relationships are weaker for precipitation amounts. Regimes also modify regional contrasts between observational sites in Kerguelen, highlighting strong exposure contrasts. The regimes allow us to improve our understanding of weather and climate variability and interactions in this region; they will be used in future work to assess past and projected long-term circulation changes in the southern midlatitudes.

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Liang Wang and Dan Li

Abstract

In this study, we simulate the magnitude of urban heat islands (UHIs) during heat wave (HWs) in two cities with contrasting climates (Boston, Massachusetts, and Phoenix, Arizona) using the Weather Research and Forecasting (WRF) Model and quantify their drivers with a newly developed attribution method. During the daytime, a surface UHI (SUHI) is found in Boston, which is mainly caused by the higher urban surface resistance that reduces the latent heat flux and the higher urban aerodynamic resistance r a that inhibits convective heat transfer between the urban surface and the lower atmosphere. In contrast, a daytime surface urban cool island is found in Phoenix, which is mainly due to the lower urban r a that facilitates convective heat transfer. In terms of near-surface air UHI (AUHI), there is almost no daytime AUHI in either city. At night, an SUHI and an AUHI are identified in Boston that are due to the stronger release of heat storage in urban areas. In comparison, the lower urban r a in Phoenix enhances convective heat transfer from the atmosphere to the urban surface at night, leading to a positive SUHI but no AUHI. Our study highlights that the magnitude of UHIs or urban cool islands is strongly controlled by urban–rural differences in terms of aerodynamic features, vegetation and moisture conditions, and heat storage, which show contrasting characteristics in different regions.

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Yuchuan Lai and David A. Dzombak

Abstract

An integrated technique combining global climate model (GCM) simulation results and a statistical time series forecasting model [the autoregressive integrated moving average (ARIMA) model] was developed to bring together the climate change signal from GCMs to city-level historical observations as an approach to obtain location-specific temperature and precipitation projections. This approach assumes that regional temperature and precipitation time series reflect a combination of an underlying climate change signal series and a regional-deviation-from-the-signal series. An ensemble of GCMs is used to describe and provide the climate change signal, and the ARIMA model is used to model and project the regional deviation. Qualitative and quantitative assessments were conducted for evaluating the projection performance of the hybrid GCM-ARIMA (G-ARIMA) model. The results indicate that the G-ARIMA model can provide projected city-specific daily temperature and precipitation series comparable to historical observations and can have improved projection accuracy for several assessed annual indices compared to a commonly used downscaled projection product. The G-ARIMA model is subject to some limitations and uncertainties from the GCM-provided climate change signal. A notable feature of the G-ARIMA model is the efficiency with which projections can be updated when new observations become available, thus facilitating updating of regional temperature and precipitations projections. Given the increasing need for and use of location-specific climate projections in practical engineering applications, the G-ARIMA model is an option for regional temperature and precipitation projection for such applications.

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Bhupendra A. Raut, Robert Jackson, Mark Picel, Scott M. Collis, Martin Bergemann, and Christian Jakob

Abstract

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

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Jinqin Xu, Yan Zeng, Xinfa Qiu, Yongjian He, Guoping Shi, and Xiaochen Zhu

Abstract

Drylands cover about one-half of the land surface in China and are highly sensitive to climate change. Understanding climate change and its impact drivers on dryland is essential for supporting dryland planning and sustainable development. Using meteorological observations for 1960–2019, the aridity changes in drylands of China were evaluated using aridity index (AI), and the impact of various climatic factors [i.e., precipitation P; sunshine duration (SSD); relative humidity (RH); maximum temperature (Tmax); minimum temperature (Tmin); wind speed (WS)] on the aridity changes was decomposed and quantified. Results of trend analysis based on Sen’s slope estimator and Mann–Kendall test indicated that the aridity trends were very weak when averaged over the whole drylands in China during 1960–2019 but exhibited a significant wetting trend in hyperarid and arid regions of drylands. The AI was most sensitive to changes in water factors (i.e., P and RH), followed by SSD, Tmax, and WS, but the sensitivity of AI to Tmin was very small and negligible. Interestingly, the dominant climatic driver to AI change varied in the four dryland subtypes. The significantly increased P dominated the increase in AI in the hyperarid and arid regions. The significantly reduced WS and the significantly increased Tmax contributed more to AI changes than the P in the semiarid and dry subhumid regions of drylands. Previous studies emphasized the impact of precipitation and temperature on the global or regional dry–wet changes; however, the findings of this study suggest that, beyond precipitation and temperature, the impact of wind speed on aridity changes of drylands in China should be given equal attention.

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Liang Chang, Shiqiang Wen, Guoping Gao, Zhen Han, Guiping Feng, and Yang Zhang

Abstract

Characteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

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