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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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Maike F. Holthuijzen, Brian Beckage, Patrick J. Clemins, Dave Higdon, and Jonathan M. Winter

Abstract

High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

Open access
Cristian Muñoz and David M. Schultz

Abstract

A study of 500-hPa cutoff lows in central Chile during 1979–2017 was conducted to contrast cutoff lows associated with the lowest quartile of daily precipitation amounts (LOW25) with cutoff lows associated with the highest quartile (HIGH25). To understand the differences between low- and high-precipitation cutoff lows, daily precipitation records, radiosonde observations, and reanalyses were used to analyze the three ingredients necessary for deep moist convection (instability, lift, and moisture) at the eastern and equatorial edge of these lows. Instability was generally small, if any, and showed no major differences between LOW25 and HIGH25 events. Synoptic-scale ascent associated with Q-vector convergence also showed little difference between LOW25 and HIGH25 events. In contrast, the moisture distribution around LOW25 and HIGH25 cutoff lows was different, with a moisture plume that was more defined and more intense equatorward of HIGH25 cutoff lows as compared with LOW25 cutoff lows where the moisture plume occurred poleward of the cutoff low. The presence of the moisture plume equatorward of HIGH25 cutoff lows may have contributed to the shorter persistence of HIGH25 events by providing a source for latent-heat release when the moisture plume reached the windward side of the Andes. Indeed, whereas 48% of LOW25 cutoff lows persisted for longer than 72 h, only 25% of HIGH25 cutoff lows did, despite both systems occurring mostly during the rainy season (May–September). The occurrence of an equatorial moisture plume on the eastern and equatorial edge of cutoff lows is fairly common during high-impact precipitation events, and this mechanism could help to explain high-impact precipitation where the occurrence of cutoff lows and moisture plumes is frequent.

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Juan A. Crespo, Catherine M. Naud, and Derek J. Posselt

Abstract

Latent and sensible heat fluxes over the oceans are believed to play an important role in the genesis and evolution of marine-based extratropical cyclones (ETCs) and affect rapid cyclogenesis. Observations of ocean surface heat fluxes are limited from existing in situ and remote sensing platforms, which may not offer sufficient spatial and temporal resolution. In addition, substantial precipitation frequently veils the ocean surface around ETCs, limiting the capacity of spaceborne instruments to observe the surface processes within maturing ETCs. Although designed as a tropics-focused mission, the Cyclone Global Navigation Satellite System (CYGNSS) can observe ocean surface wind speed and heat fluxes within a notable quantity of low-latitude extratropical fronts and cyclones. These observations can assist in understanding how surface processes may play a role in cyclogenesis and evolution. This paper illustrates CYGNSS’s capability to observe extratropical cyclones manifesting in various ocean basins throughout the globe and shows that the observations provide a robust sample of ETCs winds and surface fluxes, as compared with a reanalysis dataset.

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