Subseasonal prediction of impactful California weather in a hybrid dynamical-statistical framework DOI Open Access
Kristen Guirguis, Alexander Gershunov, Benjamin J. Hatchett

et al.

Authorea (Authorea), Journal Year: 2023, Volume and Issue: unknown

Published: July 20, 2023

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts resource management would benefit from extending lead times of skillful prediction these other types extreme patterns. Here we describe a methodology subseasonal winter in California, including ARs, SAWs temperature extremes. The hybrid approach combines dynamical model historical information to forecast probabilities outcomes at weeks 1-4 lead. This (i) uses considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters error/uncertainty longer times, (iii) increases the sample likely by utilizing full record instead more limited suite ensemble members. We demonstrate skill above climatology timescales, highlighting potential use water, health, land, fire decision support.

Language: Английский

Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event DOI Creative Commons
Alan M. Rhoades, Colin M. Zarzycki, Benjamin J. Hatchett

et al.

Climate Dynamics, Journal Year: 2024, Volume and Issue: 62(9), P. 8615 - 8637

Published: Aug. 1, 2024

Abstract The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. joint occurrence of saturated soils, rainfall, snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, warming sensitivity drivers across scales understudied. We leverage regionally refined mesh capabilities Energy Exascale Earth System Model (RRM-E3SM) recreate horizontal grid spacings 3.5 km California, forecast lead times up 4 days, six levels ranging from pre-industrial conditions $$+3.5\,^\circ$$ + 3.5 ∘ C. describe including duration intensity, precipitation phase, intensity efficiency, snowpack mass energy changes, runoff efficiency. Our findings indicate current change negligibly influence drivers. At $$\ge 1.7\,^\circ$$ ≥ 1.7 C, hazard potential increases, nonlinearly decreases, antecedent soil moisture decreases (except where snowline retreats), southern Sierra Nevada persists). Storm total but at rates below warming-induced increases saturation-specific humidity. Warming intensifies short-duration, high-intensity particularly snowfall-to-rainfall transitions occur. This study highlights nonlinear tradeoffs 21st-century hazards provides water management infrastructure investment adaptation considerations.

Language: Английский

Citations

4
Atmospheric River Frequency‐Category Characteristics Shape U.S. West Coast Runoff DOI Creative Commons
Yang Zhou, Joshua S. North, Alan M. Rhoades

et al.

Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(2)

Published: Jan. 16, 2025

Abstract This study investigates the factors influencing runoff response to atmospheric rivers (ARs) over U.S. West Coast. We focused on time series variations impacted by AR characteristics (e.g., category and frequency) land preconditions during Northern Hemisphere cool seasons in period of 1940–2023. Results show that high‐category ARs significantly increase local with higher hourly precipitation rates leading a greater incremental rate peak runoff. Extreme increases greatly an up 12.5 times stronger than non‐extreme Besides category, such as soil moisture snowpack also play crucial roles modulating response. found induced weak‐category is more sensitive ARs, high occurring when nearly saturated. Additionally, 50% high‐peak‐runoff events snow‐covered grid cells are associated rain‐on‐snow particularly for weaker ARs. Regression analysis reveals jointly influence runoff, emphasizing importance including levels impact assessments. The highlights intensified back‐to‐back short intervals, which may become frequent climate warming, posing increased flood risks via facilitating wet conditions. Our findings have significant implications risk predictions development prediction models AR‐induced

Language: Английский

Citations

0
Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California DOI Creative Commons
Abhishekh Srivastava, Richard Grotjahn, Alan M. Rhoades

et al.

Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(5)

Published: March 6, 2025

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies days before onset, from October March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum four distinguished LSMP clusters. The clusters are characterized as Northwest continental negative anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward “PNA,” and Prominent Alaskan ridge. These clusters, shown in multiple variables, evolve very differently have differing links Arctic tropical Pacific regions. Using binary forecast skill measures new copula‐based framework for predicting PEx events, we indices useful predictors NorCal with moisture‐based variables being best at least 6 lower atmospheric better than their upper counterparts any day advance tested. To ensure statistical rigor, analyzed here (with modified acronym) include local tests both consistency, which not always featured literature meteorological patterns.

Language: Английский

Citations

0
Characterization of Western US Hydrologic Processes Linked to Atmospheric Rivers in Two Sets of Seasonal Global Retrospective Forecasts DOI Creative Commons
Breanna L. Zavadoff, Ben P. Kirtman

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Abstract Atmospheric rivers (ARs) are narrow filaments of high water vapor content that have considerable influence on the western United States (US) hydroclimate. ARs provide significant amounts annual precipitation and snowfall affect mountain snowpack via snow equivalent (SWE) accumulation ablation. With projected to become increasingly key players in US hydrology, resource managers will rely progressively more AR seasonal forecasts infer flood/drought risks make informed decisions about supply allocation. However, precisely how well current climate prediction systems capture their associated hydrologic variables is still an open question. Here, we evaluate ability (HR) low resolution (LR) CCSM4 CESM1 global retrospective characterize precipitation, snowfall, SWE changes with landfalling ARs. HR accurately represent than LR forecasts, however, CCSM4-HR underestimates AR-related causing enhanced Further investigation reveals amplified onshore positive temperature advection by south-southwesterly biased winds causes be embedded within thicker air columns, yielding increased freezing level heights, reduced loss. Results suggest both forecast models capable characterizing distribution frequency, but needed for proper representation. Furthermore, used assess hydrological processes must contain accurate wind fields, as even minor biases can a profound effect model's simulate accumulation/ablation rates.

Language: Английский

Citations

0
Divergent responses of historic rain-on-snow flood extremes to a warmer climate DOI Creative Commons

Dalei Hao,

Gautam Bisht, Donghui Xu

et al.

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: May 24, 2025

Language: Английский

Citations

0
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0 DOI Creative Commons
Jishi Zhang, Peter Bogenschutz, Qi Tang

et al.

Geoscientific model development, Journal Year: 2024, Volume and Issue: 17(9), P. 3687 - 3731

Published: May 8, 2024

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive run multi-decadal simulations. We developed a 3.25 km modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated nudging CARRM outside 1° coupled simulation E3SMv1 under Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. grid spacing adds considerable value prediction changes, including more realistic high temperatures Central Valley much improved distributions precipitation snowpack Sierra Nevada coastal stratocumulus. Under SSP5-8.5 scenario, predicts widespread warming 6–10 °C over most California, 38 % increase statewide average 30 d winter–spring precipitation, near-complete loss alpine snowpack, sharp reduction shortwave cloud radiative forcing associated with marine stratocumulus end 21st century. note climatological wet bias for discuss possible reasons. conclude that SCREAM RRM is technically feasible scientifically valid tool simulations regions interest, providing an excellent bridge

Language: Английский

Citations

3
Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convection Resolving E3SM Atmosphere Model (SCREAM) Version 0 DOI Creative Commons
Peter Bogenschutz, Jishi Zhang, Qi Tang

et al.

Published: April 30, 2024

Abstract. Using the Regionally Refined Mesh (RRM) configuration of U.S. Department Energy's Simple Cloud Resolving E3SM Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms areal extent refined mesh resolution (ranging from 800 m to 3.25 km). find that SCREAM-RRM generally has a good representation AR generated precipitation CA, even for control simulation which very small 3 km patch, is able capture fine scale regional distributions are controlled largely by topography state. Although, it found SCREAM wet bias topography, most prominently Sierra Nevada mountain range, with corresponding dry on lee side. refining beyond (specifically 1.6 m) virtually no benefit towards reducing systematic biases, but improvements can be when increasing upstream mesh. However, these relatively modest only realized if size expanded where employing longer achieves substantial cost was intended for.

Language: Английский

Citations

2
Reinterpreting ENSO's Role in Modulating Impactful Precipitation Events in California DOI Creative Commons
Kristen Guirguis, Benjamin J. Hatchett, Alexander Gershunov

et al.

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(14)

Published: July 24, 2024

Abstract Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation snow records along mid‐tropospheric circulation to identify recurring weather patterns driving California Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these patterns, which largely drives the canonical ENSO‐precipitation relationship. However, frequency is not strongly modulated remains a primary source uncertainty forecasting. Seasonal ENSO‐independent major cause anomalous WY2017, record‐setting WY2023, outcome during recent El Niño winters 1983, 1998, 2016. Improved understanding recurrent atmospheric could help improve forecasts.

Language: Английский

Citations

1
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0 DOI Creative Commons
Peter Bogenschutz, Jishi Zhang, Qi Tang

et al.

Geoscientific model development, Journal Year: 2024, Volume and Issue: 17(18), P. 7029 - 7050

Published: Sept. 19, 2024

Abstract. Using the regionally refined mesh (RRM) configuration of US Department Energy's Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms areal extent resolution (ranging from 800 m to 3.25 km). find that SCREAM generally has a good representation AR-generated precipitation CA, even for control simulation which very small 3 km patch, is able capture fine-scale regional distributions are controlled largely by topography state. It found wet bias topography, most prominently Sierra Nevada mountain range, with corresponding dry on lee side. refining beyond (specifically 1.6 m) virtually no benefit towards reducing systematic biases but improvements can be when increasing upstream mesh. However, these relatively modest only realized if size expanded scale where employing longer achieves substantial cost it was intended for.

Language: Английский

Citations

1
Anticipating How Rain-on-Snow Events Will Change through the 21st Century: Lessons from the 1997 New Year’s Flood Event DOI Creative Commons
Alan M. Rhoades, Colin M. Zarzycki, Benjamin J. Hatchett

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 6, 2024

Abstract The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. joint occurrence of saturated soils, rainfall, snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, warming sensitivity drivers across scales understudied. We leverage regionally refined mesh capabilities Energy Exascale Earth System Model (RRM-E3SM) recreate at a horizontal resolution 3.5km California, forecast lead times 2-4 days, six levels ranging from pre-industrial conditions +3.5\degree{}C. describe including duration intensity, precipitation phase, intensity efficiency, snowpack mass energy changes, runoff efficiency. Our findings indicate current change negligibly influence flood. At ≥1.7°C, hazard potential increases, nonlinearly decreases, antecedent soil moisture decreases (except where snowline retreats), southern Sierra Nevada persists). Storm total but rates below warming-induced increases saturation-specific humidity. Warming intensifies short-duration, high-intensity particularly snowfall-to-rainfall transitions occur. This study highlights nonlinear tradeoffs 21st-century hazards provides water management infrastructure investment adaptation considerations.

Language: Английский

Citations

0