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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. Additionally, 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 based on the results of the numerical experiments.

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Fraser King, George Duffy, and Christopher G. Fletcher

Abstract

Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size and distribution which contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the VertiX X-band radar instrument in Egbert, Ontario are compared with in situ surface snow accumulation measurements from January-March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and European Centre for Medium-RangeWeather Forecasts (ECMWF) Reanalysis version 5 (ERA-5) atmospheric temperature estimates, to derive a surface snow accumulation regression model. Using event-based training-testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-minute intervals with a low mean square error (MSE) of approximately 1.8×10−3 mm2 when compared to collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze − S relationships) which were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning-based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.

Open access
Julie M. Thériault, Vanessa McFadden, Hadleigh Thompson, and Mélissa Cholette

Abstract

Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting two weeks. This study aims to investigate the meteorological conditions during this severe storm and their contribution to major power outages using high-resolution atmospheric modelling. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low-pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C due to radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events.

Open access
Isabelle Renee Lao, Carsten Abraham, Ed Wiebe, and Adam H. Monahan

Abstract

Nocturnal warming events (NWEs) are abrupt interruptions in the typical cooling of surface temperatures at night. Using temperature time series from the high resolution Vancouver Island School-Based Weather station network (VWSN) in British Columbia, Canada, we investigate temporal and spatial characteristics of NWEs. In this coastal region, NWEs are more frequently detected in winter than in summer, with a seasonal shift from slowly warming NWEs dominating winter months to rapidly warming NWEs dominating the summer months. Slow warming NWEs are of relatively small amplitude and exhibit slow cooling rates after the temperature peaks. In contrast, fast warming NWEs have a temperature increase of several Kelvin with shorter duration temperature peaks. The median behaviour of these distinct NWE classes at individual stations is similar across the entire set of stations. The spatial synchronicity of NWEs across the VWSN (determined by requiring NWEs at station pairs to occur within given time windows) decreases with distance, including substantial variability at nearby stations reflecting local influences. Fast warming NWEs are observed to occur either simultaneously across a number of stations or are isolated at one station. Spatial synchronicity values are used to construct undirected networks to investigate spatial connectivity structures of NWEs. We find that, independent of individual seasons or NWE classes, the networks are largely unstructured, with no clear spatial connectivity structures related to local topography or direction.

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Tess W. P. Jacobson, Richard Seager, A. Park Williams, and Naomi Henderson

Abstract

Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years.

Significance Statement

The purpose of this study is to identify the local and global climate patterns in the preceding seasons that influence how the burned summer forest area in California varies year-to-year. We find that a dry atmosphere, high temperatures, dry soils, less snowpack, low precipitation, subsiding air, and high pressure centered west of California all correlate significantly with large summer burned area as far back as the preceding January. These climate anomalies occur as part of a hemispheric scale pattern with weak connections to the tropical Pacific Ocean. We also describe the climate anomalies preceding the extreme and record-breaking burned-area year of 2018, and how these compared with the more general patterns found. These results give important insight into how well and how early it might be possible to predict the severity of an upcoming summer wildfire season in California.

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Arbindra Khadka, Patrick Wagnon, Fanny Brun, Dibas Shrestha, Yves Lejeune, and Yves Arnaud

Abstract

We present a multisite evaluation of meteorological variables in the Everest region (Nepal) from ERA5-Land and High Asian Refined Analysis, version 2 (HARv2), reanalyses in comparison with in situ observations, using classical statistical metrics. Observation data have been collected since 2010 by seven meteorological stations located on or off glacier between 4260 and 6352 m MSL in the upper Dudh Koshi basin; 2-m air temperature, specific and relative humidities, wind speed, incoming shortwave and longwave radiations, and precipitation are considered successively. Overall, both gridded datasets are able to resolve the mesoscale atmospheric processes, with a slightly better performance for HARv2 than that for ERA5-Land, especially for wind speed. Because of the complex topography, they fail to reproduce local- to microscale processes captured at individual meteorological stations, especially for variables that have a large spatial variability such as precipitation or wind speed. Air temperature is the variable that is best captured by reanalyses, as long as an appropriate elevational gradient of air temperature above ground, spatiotemporally variable and preferentially assessed by local observations, is used to extrapolate it vertically. A cold bias is still observed but attenuated over clean-ice glaciers. The atmospheric water content is well represented by both gridded datasets even though we observe a small humid bias, slightly more important for ERA5-Land than for HARv2, and a spectacular overestimation of precipitation during the monsoon. The agreement between reanalyzed and observed shortwave and longwave incoming radiations depends on the elevation difference between the station site and the reanalysis grid cell. The seasonality of wind speed is only captured by HARv2. The two gridded datasets ERA5-Land and HARv2 are applicable for glacier mass and energy balance studies, as long as either statistical or dynamical downscaling techniques are used to resolve the scale mismatch between coarse mesoscale grids and fine-scale grids or individual sites.

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Logan R. Bundy, Vittorio A. Gensini, and Mark S. Russo

Abstract

This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions.

Significance Statement

This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate.

Open access
Matthew Lebsock, Hanii Takahashi, Richard Roy, Marcin J. Kurowski, and Lazaros Oreopoulos

Abstract

An algorithm that derives the nonprecipitating cloud liquid water path W cld from CloudSat using a surface reference technique (SRT) is presented. The uncertainty characteristics of the SRT are evaluated. It is demonstrated that an accurate analytical formulation for the pixel-scale precision can be derived. The average precision of the SRT is estimated to be 34 g m−2 at the individual pixel scale; however, precision systematically decreases from around 30 to 40 g m−2 as cloud fraction varies from 0% to 100%. The retrievals of clear-sky W cld have a mean bias of 0.9 g m−2. Output from a large-eddy simulation coupled to a radar simulator shows that an additional bias of −8% may result from nonuniformity within the footprint of cloudy pixels. The retrieval yield for the SRT, measured relative to all warm clouds over ocean between 60°N and 60°S latitude is 43%. The SRT W cld is compared with one estimate of W cld from the Moderate Resolution Imaging Spectroradiometer (MODIS) using an adiabatic cloud profile and an effective radius derived from 3.7-μm reflectance. A strong correlation between the mean MODIS W cld and SRT W cld is found across diverse cloud regimes, but with biases in the mean W cld that are cloud-regime dependent. Overall, the mean bias of the SRT relative to MODIS is −13.1 g m−2. Systematic underestimates of W cld by the SRT resulting from nonuniform beamfilling cannot be ruled out as an explanation for the retrieval bias.

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Seung-Hee Ham, Seiji Kato, Fred G. Rose, Sunny Sun-Mack, Yan Chen, Walter F. Miller, and Ryan C. Scott

Abstract

Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns due to the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth (VSCOD), defined as a visible cloud optical depth multiplied by (1–asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multi-sensor-combined cloud profiles significantly reduce positive TOA SW BB biases, compared to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 µm), 1.04–1.90 µm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.

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Hilde Haakenstad and Øyvind Breivik

Abstract

NORA3 is a convection-permitting, non-hydrostatic 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 to 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 hindcast 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 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.

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