Publications

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2019
Aguilera, R, Gershunov A, Benmarhnia T.  2019.  Atmospheric rivers impact California's coastal water quality via extreme precipitation. Science of the Total Environment. 671:488-494.   10.1016/j.scitotenv.2019.03.318   AbstractWebsite

Precipitation in California is projected to become more volatile: less frequent but more extreme as global warming pushes midlatitude frontal cyclones further poleward while bolstering the atmospheric rivers (ARs), which tend to produce the region's extreme rainfall. Pollutant accumulation and delivery to coastal waters can be expected to increase, as lengthening dry spells will be increasingly punctuated by more extreme precipitation events. Coastal pollution exposes human populations to high levels of fecal bacteria and associated pathogens, which can cause a variety of health impacts. Consequently, studying the impact of atmospheric rivers as the mechanism generating pulses of water pollution in coastal areas is relevant for public health and in the context of climate change. We aimed to quantify the links between precipitation events and water quality in order to explore meteorological causes as first steps toward effective early warning systems for the benefit of population health in California and beyond. We used historical gridded daily precipitation and weekly multiple fecal bacteria indicators at similar to 500 monitoring locations in California's coastal waters to identify weekly associations between precipitation and water quality during 2003-09 using canonical correlation analysis to account for the nested/clustered nature of longitudinal data. We then quantified, using a recently published catalog of atmospheric rivers, the proportion of coastal pollution events attributable to ARs. Association between precipitation and fecal bacteria was strongest in Southern California. Over two-thirds of coastal water pollution spikes exceeding one standard deviation were associated with ARs. This work highlights the importance of skillful AR landfall predictions in reducing vulnerability to extreme weather improving resilience of human populations in a varying and changing climate. Quantifying the impacts of ARs on waterborne diseases is important for planning effective preventive strategies for public health. (C) 2019 Elsevier B.V. All rights reserved.

Guzman-Morales, J, Gershunov A.  2019.  Climate change suppresses Santa Ana winds of Southern California and sharpens their seasonality. Geophysical Research Letters. 46:2772-2780.   10.1029/2018gl080261   AbstractWebsite

We downscale Santa Ana winds (SAWs) from eight global climate models (GCMs) and validate key aspects of their climatology over the historical period. We then assess SAW evolution and behavior through the 21st century, paying special attention to changes in their extreme occurrences. All GCMs project decreases in SAW activity, starting in the early 21st century, which are commensurate with decreases in the southwestward pressure gradient force that drives these winds. The trend is most pronounced in the early and late SAW season: fall and spring. It is mainly determined by changes in the frequency of SAW events, less so by changes in their intensity. The peak of the SAW season (November-December-January) is least affected by anthropogenic climate change in GCM projections. Plain Language Summary Dry and gusty Santa Ana winds (SAWs) drive the most catastrophic wildfires in Southern California. Their sensitivity to the changing climate has been a matter of uncertainty and debate. We have assessed the response of SAW activity to global warming and describe these results in detail here. The overall decrease in SAW activity robustly projected by downscaled global climate models is strongest in the early and late seasons-fall and spring. SAWs are expected to decrease least at the peak of their season approximately December. Importantly, decreased SAW activity in the future climate is driven mainly by decreased frequency rather than the peak intensity of these winds. These results, together with what we know from recent literature about how precipitation is projected to change in this region, suggest a later wildfire season in the future.

2018
Shields, CA, Rutz JJ, Leung LY, Ralph FM, Wehner M, Kawzenuk B, Lora JM, McClenny E, Osborne T, Payne AE, Ullrich P, Gershunov A, Goldenson N, Guan B, Qian Y, Ramos AM, Sarangi C, Sellars S, Gorodetskaya I, Kashinath K, Kurlin V, Mahoney K, Muszynski G, Pierce R, Subramanian AC, Tome R, Waliser D, Walton D, Wick G, Wilson A, Lavers D, Prabhat, Collow A, Krishnan H, Magnusdottir G, Nguyen P.  2018.  Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design. Geoscientific Model Development. 11:2455-2474.   10.5194/gmd-11-2455-2018   AbstractWebsite

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month "proof-of-concept" trial run designed to illustrate the utility and feasibility of the ARTMIP project.

Clemesha, RES, Guirguis K, Gershunov A, Small IJ, Tardy A.  2018.  California heat waves: their spatial evolution, variation, and coastal modulation by low clouds. Climate Dynamics. 50:4285-4301.   10.1007/s00382-017-3875-7   AbstractWebsite

We examine the spatial and temporal evolution of heat waves through California and consider one of the key modulating factors of summertime coastal climate-coastal low cloudiness (CLC). Heat waves are defined relative to daytime maximum temperature (T-max) anomalies after removing local seasonality and capture unseasonably warm events during May-September. California is home to several diverse climate regions and characteristics of extreme heat events are also variable throughout these regions. Heat wave events tend to be shorter, but more anomalously intense along the coast. Heat waves typically impact both coastal and inland regions, although there is more propensity towards coastally trapped events. Most heat waves with a strong impact across regions start at the coast, proceed inland, and weaken at the coast before letting up inland. Typically, the beginning of coastal heat waves are associated with a loss of CLC, followed by a strong rebound of CLC starting close to the peak in heat wave intensity. The degree to which an inland heat wave is expressed at the coast is associated with the presence of these low clouds. Inland heat waves that have very little expression at the coast tend to have CLC present and an elevated inversion base height compared with other heat waves.

Sherbakov, T, Malig B, Guirguis K, Gershunov A, Basu R.  2018.  Ambient temperature and added heat wave effects on hospitalizations in California from 1999 to 2009. Environmental Research. 160:83-90.   10.1016/j.envres.2017.08.052   AbstractWebsite

Investigators have examined how heat waves or incremental changes in temperature affect health outcomes, but few have examined both simultaneously. We utilized distributed lag nonlinear models (DLNM) to explore temperature associations and evaluate possible added heat wave effects on hospitalizations in 16 climate zones throughout California from May through October 1999-2009. We define heat waves as a period when daily mean temperatures were above the zone- and month-specific 95th percentile for at least two consecutive days. DLNMs were used to estimate climate zone-specific non-linear temperature and heat wave effects, which were then combined using random effects meta-analysis to produce an overall estimate for each. With higher temperatures, admissions for acute renal failure, appendicitis, dehydration, ischemic stroke, mental health, noninfectious enteritis, and primary diabetes were significantly increased, with added effects from heat waves observed for acute renal failure and dehydration. Higher temperatures also predicted statistically significant decreases in hypertension admissions, respiratory admissions, and respiratory diseases with secondary diagnoses of diabetes, though heat waves independently predicted an added increase in risk for both respiratory types. Our findings provide evidence that both heat wave and temperature exposures can exert effects independently.

2016
Grotjahn, R, Black R, Leung R, Wehner MF, Barlow M, Bosilovich M, Gershunov A, Gutowski WJ, Gyakum JR, Katz RW, Lee YY, Lim YK, Prabhat.  2016.  North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends. Climate Dynamics. 46:1151-1184.   10.1007/s00382-015-2638-6   AbstractWebsite

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

2015
Cavanaugh, NR, Gershunov A, Panorska AK, Kozubowski TJ.  2015.  The probability distribution of intense daily precipitation. Geophysical Research Letters. 42:1560-1567.   10.1002/2015gl063238   AbstractWebsite

The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical and practical implications for the modeling of high-frequency climate variability worldwide.

2014
Schwartz, RE, Gershunov A, Iacobellis SF, Cayan DR.  2014.  North American west coast summer low cloudiness: Broadscale variability associated with sea surface temperature. Geophysical Research Letters. 41:3307-3314.   10.1002/2014gl059825   AbstractWebsite

Six decades of observations at 20 coastal airports, from Alaska to southern California, reveal coherent interannual to interdecadal variation of coastal low cloudiness (CLC) from summer to summer over this broad region. The leading mode of CLC variability represents coherent variation, accounting for nearly 40% of the total CLC variance spanning 1950-2012. This leading mode and the majority of individual airports exhibit decreased low cloudiness from the earlier to the later part of the record. Exploring climatic controls on CLC, we identify North Pacific Sea Surface Temperature anomalies, largely in the form of the Pacific Decadal Oscillation (PDO) as well correlated with, and evidently helping to organize, the coherent patterns of summer coastal cloud variability. Links from the PDO to summer CLC appear a few months in advance of the summer. These associations hold up consistently in interannual and interdecadal frequencies.

Guirguis, K, Gershunov A, Tardy A, Basu R.  2014.  The impact of recent heat waves on human health in California. Journal of Applied Meteorology and Climatology. 53:3-19.   10.1175/jamc-d-13-0130.1   AbstractWebsite

This study examines the health impacts of recent heat waves statewide and for six subregions of California: the north and south coasts, the Central Valley, the Mojave Desert, southern deserts, and northern forests. By using canonical correlation analysis applied to daily maximum temperatures and morbidity data in the form of unscheduled hospitalizations from 1999 to 2009, 19 heat waves spanning 3-15 days in duration that had a significant impact on health were identified. On average, hospital admissions were found to increase by 7% on the peak heat-wave day, with a significant impact seen for several disease categories, including cardiovascular disease, respiratory disease, dehydration, acute renal failure, heat illness, and mental health. Statewide, there were 11 000 excess hospitalizations that were due to extreme heat over the period, yet the majority of impactful events were not accompanied by a heat advisory or warning from the National Weather Service. On a regional basis, the strongest health impacts are seen in the Central Valley and the north and south coasts. The north coast contributes disproportionately to the statewide health impact during heat waves, with a 10.5% increase in daily morbidity at heat-wave peak as compared with 8.1% for the Central Valley and 5.6% for the south coast. The temperature threshold at which an impact is seen varies by subregion and timing within the season. These results suggest that heat-warning criteria should consider local percentile thresholds to account for acclimation to local climatological conditions as well as the seasonal timing of a forecast heat wave.

2012
Gershunov, A, Guirguis K.  2012.  California heat waves in the present and future. Geophysical Research Letters. 39   10.1029/2012gl052979   AbstractWebsite

Current and projected heat waves are examined over California and its sub-regions in observations and downscaled global climate model (GCM) simulations. California heat wave activity falls into two distinct types: (1) typically dry daytime heat waves and (2) humid nighttime-accentuated events (Type I and Type II, respectively). The four GCMs considered project Type II heat waves to intensify more with climate change than the historically characteristic Type I events, although both types are projected to increase. This trend is already clearly observed and simulated to various degrees over all sub-regions of California. Part of the intensification in heat wave activity is due directly to mean warming. However, when one considers non-stationarity in daily temperature variance, desert heat waves are expected to become progressively and relatively less intense while coastal heat waves are projected to intensify even relative to the background warming. This result generally holds for both types of heat waves across models. Given the high coastal population density and low acclimatization to heat, especially humid heat, this trend bodes ill for coastal communities, jeopardizing public health and stressing energy resources. Citation: Gershunov, A., and K. Guirguis (2012), California heat waves in the present and future, Geophys. Res. Lett., 39, L18710, doi:10.1029/2012GL052979.

2008
White, WB, Gershunov A, Annis J.  2008.  Climatic influences on Midwest drought during the twentieth century. Journal of Climate. 21:517-531.   10.1175/2007jcli1465.1   AbstractWebsite

The Dustbowl Era drought in the 1930s was the principal Midwest drought of the twentieth century, occurring primarily in late spring-summer [April-August (AMJJA)] when > 70% of annual rainfall normally occurred. Another major Midwest drought occurred in the 1950s but primarily in fall-early winter [September-December (SOND)] when normal rainfall was similar to 1/2 as much. Optimized canonical correlation analysis (CCA) is applied to forecast AMJJA and SOND Midwest rainfall variability in cross-validated fashion from antecedent DJF and JJA sea surface temperature (SST) variability in the surrounding oceans. These CCA models simulate (i. e., hindcast, not forecast) the Dustbowl Era drought of the 1930s and four of seven secondary AMJJA droughts (>= 3-yr duration) during the twentieth century, and the principal Midwest drought of the 1950s and one of three secondary SOND droughts. Diagnosing the model canonical correlations finds the superposition of tropical Pacific cool phases of the quasi-decadal oscillation (QDO) and interdecadal oscillation (IDO) responsible for secondary droughts in AMJJA when ENSO was weak and finds the eastern equatorial Pacific cool phase of the ENSO responsible for secondary droughts during SOND when ENSO was strong. These explain why secondary droughts in AMJJA occurred more often (nearly every decade) and were of longer duration than secondary droughts in SOND when decadal drought tendencies were usually interrupted by ENSO. These diagnostics also find the AMJJA Dustbowl Era drought in the 1930s and the principal SOND drought in the 1950s driven primarily by different phases (i. e., in quadrature) of the pentadecadal signal in the Pacific decadal oscillation (PDO).

2005
Alfaro, EJ, Pierce DW, Steinemann AC, Gershunov A.  2005.  Relationships between the irrigation-pumping electrical loads and the local climate in Climate Division 9, Idaho. Journal of Applied Meteorology. 44:1972-1978.   10.1175/jam2315.1   AbstractWebsite

The electrical load from irrigation pumps is an important part of the overall electricity demand in many agricultural areas of the U.S. west. The date the pumps turn on and the total electrical load they present over the summer varies from year to year, partly because of climate fluctuations. Predicting this variability would be useful to electricity producers that supply the region. This work presents a contingency analysis and linear regression scheme for forecasting summertime irrigation pump loads in southeastern Idaho. The basis of the predictability is the persistence of spring soil moisture conditions into summer, and the effect it has on summer temperatures. There is a strong contemporaneous relationship between soil moisture and temperature in the summer and total summer pump electrical loads so that a reasonable prediction of summer pump electrical loads based on spring soil moisture conditions can be obtained in the region. If one assumes that decision makers will take appropriate actions based on the forecast output, the net economic benefit of forecast information is approximately $2.5 million per year, making this prediction problem an important seasonal summer forecasting issue with significant economic implications.

2004
White, WB, Gershunov A, Annis JL, McKeon G, Syktus J.  2004.  Forecasting Australian drought using southern, hemisphere modes of sea-surface temperature variability. International Journal of Climatology. 24:1911-1927.   10.1002/joc.1091   AbstractWebsite

Drought of 3 to 7 years' duration has devastated the flora, fauna, and regional economies in rangeland grazing districts over eastern and central Australia every 15 to 25 years throughout the 20th century, in some cases degrading the land beyond recover. Recently, these drought and degradation episodes have been associated with a global interdecadal oscillation (IDO) of period 15 to 25 years. This IDO signal brought cooler sea-surface temperatures (SSTs) to the western extra-tropical South Pacific Ocean in association with reduced onshore transport of moisture over eastern/central Australia during the summer monsoon. Here, we utilize optimized canonical correlation analysis (CCA) to forecase principal components of summer precipitation (PCP) anomalies over Australia from the persistence of principal components that dominate spring SST anomalies across the Southern Hemisphere. These summer PCP forecasts are cross-validated with the CCA forecast model for each year independent of that year's variability. Resulting cross-validated forecasts are best over Queensland, correlating with those observed at >0.40 from 1890 through to 2001, significant at >99% confidence level. More importantly, 6 of 10 drought episodes (but only three of seven degradation episodes) observed in eastern/central Australia during the 20th century are forecast. Copyright (C) 2004 Royal Meteorological Society.

2002
Westerling, AL, Gershunov A, Cayan DR, Barnett TP.  2002.  Long lead statistical forecasts of area burned in western US wildfires by ecosystem province. International Journal of Wildland Fire. 11:257-266.   10.1071/wf02009   AbstractWebsite

A statistical forecast methodology exploits large-scale patterns in monthly U.S. Climatological Division Palmer Drought Severity Index (PDSI) values over a wide region and several seasons to predict area burned in western US. wildfires by ecosystem province a season in advance. The forecast model, which is based on canonical correlations, indicates that a few characteristic patterns determine predicted wildfire season area burned. Strong negative associations between anomalous soil moisture (inferred from PDSI) immediately prior to the fire season and area burned dominate in most higher elevation forested provinces, while strong positive associations between anomalous soil moisture a year prior to the fire season and area burned dominate in desert and shrub and grassland provinces. In much of the western US., above- and below-normal fire season forecasts were successful 57% of the time or better, as compared with a 33% skill for a random guess, and with a low probability of being surprised by a fire season at the opposite extreme of that forecast.