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Meagan Carney
,
Holger Kantz
, and
Matthew Nicol

Abstract

Particularly important to hurricane risk assessment for coastal regions is finding accurate approximations of return probabilities of maximum wind speeds. Since extremes in maximum wind speed have a direct relationship with minima in the central pressure, accurate wind speed return estimates rely heavily on proper modeling of the central pressure minima. Using the HURDAT2 database, we show that the central pressure minima of hurricane events can be appropriately modeled by a nonstationary extreme value distribution. We also provide and validate a Poisson distribution with a nonstationary rate parameter to model returns of hurricane events. Using our nonstationary models and numerical simulation techniques from established literature, we perform a simulation study to model returns of maximum wind speeds of hurricane events along the North Atlantic coast. We show that our revised model agrees with current data and results in an expectation of higher maximum wind speeds for all regions along the coast, with the highest maximum wind speeds occurring along the northeast seaboard.

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Nischal
,
Raju Attada
, and
Kieran M. R. Hunt

Abstract

Considerable uncertainties are associated with precipitation characteristics over the western Himalayan region (WHR). These are due to typically small-scale but high-intensity storms caused by the complex topography that are under-resolved by a sparse gauge network. Additionally, both satellite and gauge precipitation measurements remain subject to systematic errors, typically resulting in underestimation over mountainous terrains. Reanalysis datasets provide prospective alternative but are limited by their resolution, which has so far been too coarse to properly resolve orographic precipitation. In this study, we evaluate and cross compare Indian Monsoon Data Assimilation and Analysis (IMDAA), the first high-resolution (12 km) regional reanalysis over India, with various precipitation products during winter season over WHR. We demonstrate IMDAA’s efficiency in representing winter precipitation characteristics at seasonal, diurnal, interannual scales, as well as heavy precipitation associated with western disturbances (WDs). IMDAA shows closer agreement to other reanalyses than to gauge-based and satellite products in error and bias analysis. Although depicting higher magnitudes, its fine resolution allows a much closer insight into localized spatial patterns and the diurnal cycle, a key advantage over other datasets. Mean winter precipitation over WHR shows a significant decreasing trend in IMDAA, despite no significant trend in the frequency of WDs tracked in either IMDAA or ERA5. The study also exhibits the potential use of IMDAA for characterizing winter atmospheric dynamics, both for climatological studies and during WD activity such as localized valley winds. Overall, these findings highlight the potential utility for IMDAA in conducting monitoring and climate change impact assessment studies over the fragile western Himalayan ecosystem.

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Patrick Hawbecker
and
Jason C. Knievel

Abstract

Simulations of Chesapeake Bay breezes are performed with varying water surface temperature (WST) datasets and formulations for the diurnal cycle of WST to determine whether more accurate depictions of water surface temperature improve prediction of bay breezes. The accuracy of simulations is measured against observed WST, inland wind speed and temperature, and in simulations’ ability to detect bay breezes via a detection algorithm developed for numerical model output. Missing WST data are found to be problematic within the Weather Research and Forecasting (WRF) Model framework, especially when activating the prognostic equation for skin temperature, sst_skin. This is alleviated when filling all missing WST values with skin temperature values within the initial and boundary conditions. Performance of bay-breeze prediction is shown to be somewhat associated with the resolution of the WST dataset. Further, model performance in simulating WST as well as in simulating the Chesapeake Bay breeze is improved when diurnal fluctuations of WST are considered via the sst skin option. Prior to running simulations, model performance in simulating the bay breeze can be accurately predicted through the use of a simple formulation.

Open access
Hilde Haakenstad
and
Øyvind Breivik

Abstract

The 3-km Norwegian Reanalysis (NORA3) is a convection-permitting, nonhydrostatic hindcast for the North Sea, the Norwegian Sea, and the Barents Sea as well as the Scandinavian Peninsula. It has a horizontal resolution of 3 km and provides a full three-dimensional atmospheric state for the period 1995–2020 with a surface analysis and boundary conditions from ERA5, a global reanalysis. In complex terrain it is found to outperform both the host reanalysis ERA5 and also the earlier hydrostatic 10-km Norwegian Hindcast Archive (NORA10), in terms of 2-m temperature and daily precipitation. Of particular interest is the representation of extreme rainfall. It is found that the upper percentiles are much better represented than in ERA5, with very little bias up to 99.9%, suggesting that the new hindcast archive is well suited for hydrological mapping and extreme-value analysis of rainfall in complex terrain.

Significance Statement

High-resolution hindcasts that permit realistic convection allow very detailed modeling of the surface temperature, precipitation, and wind field in complex terrain. There is a need for detailed mapping of rainfall and temperature extremes (upper percentiles) to assess the impact of rapid climate change. We present an assessment of the performance of the model from Part I for near-surface temperature and precipitation, which are found to be much improved in comparison with ERA5 and with earlier NORA10. The focus is on the Norwegian mainland and the Svalbard Archipelago, because the complex terrain found in these regions is challenging to represent in weather prediction models. The improvement in precipitation statistics is particularly pronounced, with nearly unbiased results up to the 99.9th percentile.

Open access
Rui Ito
,
Hiroaki Kawase
, and
Yukiko Imada

Abstract

Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there.

Significance Statement

Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world.

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Juha Tonttila
,
Anniina Korpinen
,
Harri Kokkola
,
Sami Romakkaniemi
,
Carl Fortelius
, and
Hannele Korhonen

Abstract

Intentional release of hygroscopic particles, or seeding, in convective clouds is one of the postulated methods to artificially enhance rainfall. Motivated by the general uncertainty in the underlying physics, this work employs a large-eddy simulation code together with a detailed aerosol–cloud microphysics model to investigate the conditions and processes conducive to seeding in the United Arab Emirates. Mixed-phase processes are identified as the main source for rainfall in convective clouds in this area owing to the continental aerosol characteristics and a high cloud-base altitude relatively close to the freezing level. Subsequently, our model experiments highlight the importance of mixed-phase processes in mediating the effects of hygroscopic seeding on rainfall as well. The seeding particles acted to accelerate riming by increasing the number of large droplets taken above the freezing level by the convective updrafts. The rime fraction was increased by up to 15%, which promotes the growth of the frozen hydrometeors, eventually leading to enhanced rainfall via melting. The peak enhancement in surface rainfall was up to 20%–30%, although this is almost certainly an overestimation relative to real-world operations because of the simplified description of the seeding in the model. The strongest rain enhancement was obtained with a high background aerosol concentration of approximately 4500 cm−3, whereas reduced aerosol resulted in weaker enhancement. The latter case showed an overall higher rime fraction indicating an already efficient precipitation formation process, which suppressed the seeding-induced enhancement. The conclusions of our work encourage more careful consideration of the mixed-phase processes in quantifying the hygroscopic seeding effects in continental convective clouds.

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Takeshi Watanabe
,
Kazutaka Oka
, and
Yasuaki Hijioka

Abstract

The evaluation of the representation of the surface downward shortwave flux (DSF) from atmospheric reanalysis data products is required to obtain reliable information for the resource assessment of surface solar energy. The representation of the DSF from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data product was evaluated using surface solar radiation from ground-based observations in Japan. The cloud fraction (CFR) and cloud optical thickness (COT) from Moderate Resolution Imaging Spectroradiometer (MODIS) were also used as references. The CFR from MERRA-2 tends to be smaller than that from MODIS, and the correlation between the difference in the CFR and that in the DSF is negative. The correlation between the difference in the COT and that in the DSF is weakly negative. To quantify the effects of the difference in the CFR and COT to that of the DSF, a regression model based on an artificial neural network architecture that emulates the process of the DSF in MERRA-2 was constructed. Numerical experiments using the emulator quantify contributions of each of the differences in the CFR and COT and joint contributions of the two variables. In addition, a cluster analysis was performed to clarify the differences in the seasonal changes in the monthly mean bias error (MBE) in the DSF among ground observation stations, and three clusters were identified. Contributions of the differences in the CFR and COT to the seasonal change in the monthly MBE were also clarified using the results of the numerical experiments.

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Brandon J. Daub
and
Neil P. Lareau

Abstract

In this study, we examine variations in boundary layer processes spanning the shallow-to-deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud-layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provide the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection. In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense but are weak when compared with the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.

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Kevin Gallo
and
Praveena Krishnan

Abstract

Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.

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Deon Terblanche
,
Amanda Lynch
,
Zihan Chen
, and
Scott Sinclair

Abstract

Patterns of freshwater availability—its variability and distribution—are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region.

Significance Statement

High-resolution gridded datasets are invaluable tools for gaining improved understanding of historical rainfall variations under the influence of climate change. In addition, these datasets provide consistent information for purposes such as water resources management. Quantification of dataset biases provides important guidance for robust decision-making as well as for the development of future climate scenarios. However, rainfall is an especially challenging quantity to assess. With the increasing incidence of drought and flood, methods that independently validate this high-resolution gridded data are needed to ensure high-quality knowledge support. This study demonstrates an approach using convergent streams of evidence to assess the European Centre for Medium-Range Weather Forecasts gridded rainfall dataset with the purpose of better understanding the evolving characteristics of rainfall in southern Africa.

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