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John R. Christy

Abstract

Time series of snowfall observations from over 500 stations in Oregon (OR) and Washington (WA) were generated for subregions of these states. Data problems encountered were as follows: 1) monthly totals in printed reports prior to 1940 that were not in the digital archive, 2) archived data listed as “missing” that were available, 3) digitized reports after 2010 eliminated good data, and 4) “zero” totals incorrectly entered in the official archive rather than “missing,” especially after 1980. Though addressing these was done, there is reduced confidence that some regional time series are representative of true long-term trends, especially for regions with few systematically reporting stations. For most regions characterized by consistent monitoring and with the most robust statistical reproducibility, we find no statistically significant trends in their periods of record (up to 131 years) for November–April seasonal totals through April 2020. This result includes the main snowfall regions of the Cascade Range. However, snowfall in some lower-elevation areas of OR and WA appear to have experienced declining trends, consistent with an increase in northeastern Pacific Ocean temperatures. Finally, previously constructed time series through April 2011 for regions in California are updated through April 2020 to include the recent, exceptionally low seasonal totals on the western slopes of the Sierra Nevada. This update indicates 2014/15 was the record lowest, 2013/14 was the 5th lowest, and 2012/13 was the 14th lowest of 142 years. Even so, the 1879–2020 linear trend in this key watershed region, though −2.6% decade−1, was not significantly different from zero due to high interannual variability and reconstruction uncertainty.

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Russell L. Horowitz
,
Karen A. McKinnon
, and
Isla R. Simpson

Abstract

Extreme heat events are a threat to human health, productivity, and food supply, so understanding their drivers is critical to adaptation and resilience. Anticyclonic circulation and certain quasi-stationary Rossby wave patterns are well known to coincide with heatwaves, and soil moisture deficits amplify extreme heat in some regions. However, the relative roles of these two factors in causing heatwaves is still unclear. Here we use constructed circulation analogs to estimate the contribution of atmospheric circulation to heatwaves in the United States in the Community Earth System Model version 1 (CESM1) preindustrial control simulations. After accounting for the component of the heatwaves explained by circulation, we explore the relationship between the residual temperature anomalies and soil moisture. We find that circulation explains over 85% of heatwave temperature anomalies in the eastern and western United States but only 75%–85% in the central United States. In this region, there is a significant negative correlation between soil moisture the week before the heatwave and the strength of the heatwave that explains additional variance. Further, for the hottest central U.S. heatwaves, positive temperature anomalies and negative soil moisture anomalies are evident over a month before heatwave onset. These results provide evidence that positive land–atmosphere feedbacks may be amplifying heatwaves in the central United States and demonstrate the geographic heterogeneity in the relative importance of the land and atmosphere for heatwave development. Analysis of future circulation and soil moisture in the CESM1 Large Ensemble indicates that, over parts of the United States, both may be trending toward greater heatwave likelihood.

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Jennie Bukowski
and
Susan C. van den Heever

Abstract

Haboobs are dust storms formed by strong surface winds in convective storm outflow boundaries, or cold pools, which can loft large quantities of mineral dust as they propagate. Both cold pools and the dust they loft are impacted by land surface properties resulting in complex surface interactions on haboobs. As a result of these additional complexities brought about by surface interactions, it is unclear which surface parameters and physical processes are important for predicting haboob intensity and dust concentrations. Here we applied the Morris one-at-a-time (MOAT) global sensitivity statistical method to an ensemble of 120 idealized simulations of daytime and nighttime haboobs to investigate the land surface properties that affect both dust mobilization and cold pool dynamics. MOAT identifies and ranks the importance of different input factors, which for the prediction of haboob strength and dust concentrations are 1) initial cold pool temperature, 2) surface type (vegetation), 3) soil type (clay content), and 4) soil moisture. The underlying physical mechanisms driving these feedbacks were then analyzed using a traditional one-at-a-time factor analysis. Time of day is significant for determining boundary layer height and dissipation via surface fluxes, leading to shallower, more intense cold pools/haboobs at night. Most of the land parameters modify the cold pool through impacts on surface fluxes, while surface type is dominated by roughness length effects. By ranking the importance of these surface factors, we have identified which variables are most sensitive and must be constrained via observations and data assimilation in numerical dust prediction models.

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Wenyu Zhou
,
L. Ruby Leung
, and
Jian Lu

Abstract

Tropical precipitation in climate models presents significant biases in both the large-scale pattern (i.e., double intertropical convergence zone bias) and local-scale characteristics (i.e., drizzling bias with too frequent drizzle/convection and reduced occurrences of no and heavy precipitation). By untangling the coupled system and analyzing the biases in precipitation, cloud, and radiation, this study shows that local-scale drizzling bias in atmospheric models can lead to large-scale double-ITCZ bias in coupled models by inducing convective-regime-dependent biases in precipitation and cloud radiative effects (CRE). The double-ITCZ bias consists of a hemispherically asymmetric component that arises from the asymmetric SST bias and a nearly symmetric component that exists in atmospheric models without the SST bias. By increasing light rain but reducing heavy rain, local-scale drizzling bias induces positive (negative) precipitation bias in the moderate (strong) convective regime, leading to the nearly symmetric wet bias in atmospheric models. By affecting the cloud profile, local-scale drizzling bias induces positive (negative) CRE bias in the stratocumulus (convective) regime in atmospheric models. Because the stratocumulus (convective) region is climatologically more pronounced in the southern (northern) tropics, the CRE bias is deemed to be hemispherically asymmetric and drives warm and wet (cold and dry) biases in the southern (northern) tropics when coupled to ocean. Our results suggest that correcting local-scale drizzling bias is critical for fixing large-scale double-ITCZ bias. The drizzling and double-ITCZ biases are not alleviated in models with mesoscale (0.25°–0.5°) or even storm-resolving (∼3 km) resolution, implying that either large-eddy simulation or fundamental improvement in small-scale subgrid parameterizations is needed.

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Utkarsh Mital
,
Dipankar Dwivedi
,
Ilhan Özgen-Xian
,
James B. Brown
, and
Carl I. Steefel

Abstract

An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points.

Significance Statement

Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

Free access
Jinqing Zuo
,
Fei Xie
,
Liuni Yang
,
Chenghu Sun
,
Lin Wang
, and
Ruhua Zhang

Abstract

The North Atlantic Oscillation (NAO) generally has an in-phase relationship with surface air temperature (SAT) anomalies over Northeast China in late winter. The present study shows that such an NAO–SAT relationship becomes stronger during easterly phases of the quasi-biennial oscillation (QBO), but is relatively weak during westerly phases. Observational evidence reveals that the modulation effect of the QBO on the NAO–SAT relationship over Northeast China is attributable to QBO-induced changes in the spatial structure of the NAO and associated stratosphere–troposphere coupling. During easterly QBO phases, the NAO has a strong connection with the Northern Hemisphere stratospheric polar vortex, facilitating a hemisphere-wide structure of the NAO and thus a downstream extension of NAO signal from the Euro–Atlantic sector toward Northeast Asia. During westerly QBO phases, however, the NAO has a limited connection with the stratospheric polar vortex. In this case, the NAO features a classically regional mode, with the signal in the SAT field mainly confined to the Euro-Atlantic sector. By examining historical simulations from six climate models participating in CMIP6 and including stratospheric processes, it is found that none of these models captures a significant difference in the spatial structure of the NAO and its connection with the stratospheric polar vortex between the easterly and westerly QBO phases in late winter. A key reason may be related to the poor performance of the models in simulating the Holton–Tan effect, which is critical for linking the QBO and NAO.

Significance Statement

The QBO is the dominant mode of equatorial stratospheric variability and important for seasonal forecasting. This study aims to better understand the surface influence of the QBO by examining the relationship between the NAO and temperature anomalies over China according to the phases of the QBO during boreal winter. Our results highlight the importance of the QBO in modulating the spatial structure and thus climate impact of the NAO via stratosphere–troposphere coupling in observations, while state-of-the-art climate models perform poorly in simulating the QBO-related stratosphere–troposphere coupling and thus the QBO–NAO connection. These findings have important implications for seasonal prediction and model development in the future.

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David M. Schultz
Free access
Bin Zheng
,
Dejun Gu
,
Ailan Lin
,
Dongdong Peng
,
Chunhui Li
, and
Yanyan Huang

Abstract

In the present study, the structures and mechanisms of the heatwaves (HWs) associated with the quasi-biweekly (QBW; 10–20-day period) variability (QBW-HW) over southern China (SC; 106°–120°E, 21°–30°N) are investigated by using observation data from surface stations in China and the related gridded dataset (CN05.1), and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. We found that the strongest anticyclonic anomaly and subsidence appear over SC during the developing phase of QBW-HW, and then induced excess solar radiation at surface and significant diabatic heating lead to a positive surface air temperature change, thus favoring occurrence of QBW-HW over SC. In addition, we found a wet near-surface atmosphere in the QBW-HW events over SC, and further confirmed that near-surface moisture should play an important role in the occurrence of QBW-HW, via absorptions of longwave and shortwave radiation. This result is quite different from previous studies since they did not pay attention to the near-surface moisture. On the other hand, warmer SAT favors more water vapor evaporated from the moist soil when considering the Clausius–Clapeyron relationship. Then, the positive feedback processes promote the occurrence of QBW-HW over SC. In contrast, during the developing and warm phases of QBW-HW over SC, except for the near-surface level, the troposphere is in a dry condition, even at 850 and 700 hPa. In the QBW-HW events over SC, the factor responsible for the wet near-surface atmosphere is the enhanced surface evaporation, which is attributed to strengthened surface wind speed and background moist soil.

Significance Statement

Under the background of global warming, heatwaves over Southern China are experiencing an increasing trend. In this study, we want to understand the structures and mechanisms of the heatwaves related to 10–20-day (quasi-biweekly) variability. We that found some structures of heatwaves (e.g., anticyclonic anomalies along with subsidence) are consistent with previous studies. In addition, we also show that the moist soil and increased induced near-surface moisture play a key role in the occurrence of heatwaves over Southern China, via enhanced absorptions of longwave and shortwave radiation. This study is helpful for understanding the processes and prediction of heatwaves over Southern China. Future work should examine the findings by some numerical experiments with a climate model.

Open access
Kathy Pegion
,
Emily J. Becker
, and
Ben P. Kirtman

Abstract

We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

Free access
Jing Zhu
,
Zhaoyong Guan
, and
Xudong Wang

Abstract

The sea surface temperature anomalies (SSTA) in the Maritime Continent (MC) region are mainly related to local variability, ENSO, and the Indian Ocean dipole. Using the reanalysis data from NOAA and NCEP–NCAR, by employing the empirical orthogonal function (EOF) analysis, we have explored the principal mode of ENSO-independent summertime SSTA in the MC and its associations with regional climate anomalies. After ENSO signals have been removed, the leading mode of SSTA in the MC exhibits a uniformly signed pattern, which mainly varies on an interannual time scale. The maintenance mechanisms of the ENSO-independent SSTA are different in different subregions, especially over the region south of Java and the tropical northwestern Pacific. When the time coefficient of the first leading EOF mode (EOF1) is positive, warmer SSTAs are observed in the area south of Java. The oceanic dynamic heating there facilitates the warmer SSTA. Thus, the Gill-type response of the atmosphere is found over the region south of Java. The diabatic cooling in the atmosphere is dominant over the tropical northwestern Pacific where the warmer SSTA is maintained by the absorption of solar radiation due to less cloud cover there. A tilted vertical circulation is hence formed, linking the tropical southeastern Indian Ocean with the tropical northwestern Pacific. The anomalous circulations in the Asian–Australian monsoon region are affected by this ENSO-independent SSTA mode, resulting in decreased summer rainfall anomaly in the region near the southeast coast of China and increased winter precipitation anomaly over the extratropical region of Australia.

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