•A weather regime-based stochastic weather generator is utilized to create thermodynamic and dynamic climate change scenarios.•The ensemble comprises 32, 1000-yr climate scenarios for changes in ...temperature, precipitation, and atmospheric circulation.•The scenarios are intended to support bottom-up climate vulnerability assessments of water systems across California.
This study is the second of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 2 of this series, we present how the model is used to develop an ensemble of climate change scenarios based on both thermodynamic and dynamic signals of climate change. The ensemble includes a suite of 30 climate change scenarios, each consisting of 1000 years of simulated daily climate data (precipitation, maximum temperature, minimum temperature) at a 6 km resolution across the entire state of California. The 30 scenarios represent a range of plausible climate changes to temperature, average precipitation, and precipitation extremes that are reflective of thermodynamic responses of the atmosphere to warming. An additional two scenarios are also created that represent changes in the frequency of weather regimes (e.g., dynamic climate change). Results from these scenarios reveal that when the effects of anthropogenic climate change are combined with plausible realizations of natural climate variability, the severity of extremes in California is amplified significantly. In addition, recent changes in the frequency of large-scale patterns of atmospheric circulation can have impacts of similar magnitude to large (>10%) declines in average precipitation, particularly with respect to drought. The scenarios developed in this work are designed to allow water managers to systematically test the sensitivity of their water system to different combinations of climate change, so that key vulnerabilities can be discovered and then addressed through adaptation planning.
•A novel weather regime-based stochastic weather generator simulates daily precipitation and temperature in California.•The model reproduces a wide range of climate statistics and extremes with high ...fidelity at various spatiotemporal scales.•The model’s strong performance statewide supports climate impact assessments on water systems across California’s watersheds.
This study is the first of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 1 of this series, we present the details of model development and validation. The model is based on the identification and simulation of weather regimes, or large-scale patterns of atmospheric flow, which are then used to condition the simulation of local, daily weather at a 6 km resolution across the state. We conduct a thorough validation of a baseline, 1000-year model simulation to evaluate its ability to accurately simulate daily precipitation and minimum and maximum temperature at various spatial scales (grid cell, river basin) and temporal scales (daily, event-based, monthly, annual, inter-annual to decadal). Results show that the model effectively reproduces a large suite of climate statistics at these scales across the entire state, including moments, spells, dry and wet extremes, and extreme hot and cold periods. Moreover, the model successfully maintains spatial correlations and inter-variable relationships, enabling the use of model simulations in hydrologic and water resources analyses that span multiple watersheds across California. The weather generator can simulate physically plausible extreme events (e.g., multi-day extreme precipitation and severe drought) that extend beyond the worst case conditions observed historically, independent of climate change. Thus, the baseline simulation can be used to understand the impacts of natural climate variability on both flood and drought risk in regional water systems. Scenarios of climate change are discussed in Part 2.
Water resources planning and management requires the estimation of extreme design events. Anticipated climate change is playing an increasingly prominent role in the planning and design of long-lived ...infrastructure, as changes to climate forcings are expected to alter the distribution of extremes in ways and to extents that are difficult to predict. One approach is to use climate projections to force hydrologic models, but this raises two challenges. First, global climate models generally focus on much larger scales than are relevant to hydrologic design, and regional climate models that better capture small scale dynamics are too computationally expensive for large ensemble analyses. Second, hydrologic models systematically misrepresent the variance and higher moments of streamflow response to climate, resulting in a mischaracterization of the extreme flows of most interest. To address both issues, we propose a new framework for non-stationary risk-based hydrologic design that combines a stochastic weather generator (SWG) that accurately replicates basin-scale weather and a stochastic watershed model (SWM) that accurately represents the distribution of extreme flows. The joint SWG-SWM framework can generate large ensembles of future hydrologic simulations under varying climate conditions, from which design statistics and their uncertainties can be estimated. The SWG-SWM framework is demonstrated for the Squannacook River in the Northeast United States. Standard approaches to design flows, like the T-year flood, are difficult to interpret under non-stationarity, but the SWG-SWM simulations can readily be adapted to risk and reliability metrics which bare the same interpretation under stationary and non-stationary conditions. As an example, we provide an analysis comparing the use of risk and more traditional T-year design events, and conclude that risk-based metrics have the potential to reduce regret of over- and under-design compared to traditional return-period based analyses.
To aid California's water sector to better understand and manage future climate extremes, we present a method for creating a regionally consistent ensemble of plausible daily future climate and ...streamflow scenarios that represent natural climate variability captured in a network of tree‐ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo‐reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin Valley of California. To assess the compound effects of climate change, we create temperature series that reflect projected scenarios of warming and precipitation series that have been scaled to reflect thermodynamically driven shifts in the distribution of daily precipitation. We then use these weather scenarios to force hydrologic models for each of the five subbasins. The paleo‐forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non‐stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision‐relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the San Joaquin.
Plain Language Summary
California experiences cycles of floods and droughts that can be driven by both natural variability and climate change. The specific role these drivers play in impacting extremes is uncertain, but can influence how to best plan and manage regional water systems for future extremes. To better quantify the role of these drivers, we introduce a framework that utilizes a 600‐year tree‐ring reconstruction to create long sequences of plausible future weather and streamflow for key basins in the San Joaquin Valley. We find that a large portion of variability in extremes can be attributed to natural variability at shorter planning horizons, but that human‐driven climate changes are influential at longer planning horizons (>30 years). Furthermore, decision‐makers' perceptions of important drivers can be skewed depending on the specific definitions used to analyze floods and droughts, which can present significant challenges for adaptation planning and infrastructure development tied to tracking hydroclimate variables. This study also illustrates the vast variability in extremes that the region has experienced over the past 600 years and highlights the pitfalls of defining risk based on a limited historical record.
Key Points
We introduce a framework to create 600‐year ensembles of future weather and streamflow for basins in the San Joaquin Valley
We discover vast variability and non‐stationarity in flood and drought extremes in the region over the past 600 years
The joint importance of climate change and natural variability in shaping floods and droughts is critical to water systems planning
This study explores multi‐scale variability in the relationship between turbidity (Tn) and flow (Q) in 162 watersheds across the contiguous United States. Sites are selected where Tn acts as a good ...surrogate for suspended sediment concentration. We use dynamic linear models (DLMs) to infer time‐varying parameters of Tn‐Q rating curves at each site, and calibrate a hyper‐parameter of the DLM model (δ $\delta $) to quantify the degree of dynamicity in the rating curve relationship. The DLM can capture dynamics in the Tn‐Q relationship at the resolution of the data (daily in this study), enabling an analysis of the dynamics across time scales. Regional multivariate regressions are used to identify physiographic features that relate to the magnitude of δ $\delta $ and spectral signatures in the DLM parameters across sites. Results show that watersheds in the Midwest and Pacific Northwest tend to exhibit more variable Tn‐Q relationships, while these relationships are more stable in watersheds in the humid east and Lower Mississippi River basin. Stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network are associated with the dynamicity in the Tn‐Q relationship. DLM parameters exhibit cyclic behavior at sub‐monthly, sub‐annual, and annual time scales at sites across the country, with annual cycling associated with basin features that reflect watershed sediment availability and the erosive power of rivers. Overall, our analysis highlights significant multi‐scale variability in Tn‐Q relationships across the nation, with important implications for how sediment dynamics should be measured and managed at the watershed‐scale.
Plain Language Summary
Effective sediment management often requires accurate sediment yield predictions and forecasts. However, the predictive skill of various models often declines in regions where the sediment transport processes are variable across timescales. This study aims to improve our understanding of variability in the relationship between turbidity (Tn) and flow (Q) through time, focusing on sites where turbidity is a good proxy for suspended sediment concentration. We use regression models with parameters that can vary over time to quantify the variability in the Tn‐Q relationship and to highlight the presence of seasonal patterns in transport processes. We then explore how watershed characteristics influence the variability and seasonality in the Tn‐Q relationship. Our results suggest that stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network influence the overall variability in the Tn‐Q relationship, while the seasonality is mainly associated with basin features that reflect watershed sediment availability and the erosive power of rivers. The results of the study have important implications for how sediment dynamics should be addressed in watershed management studies.
Key Points
The dynamicity of sediment transport processes is quantified using dynamic linear rating curve models of turbidity and flow
The degree of rating curve dynamicity varies spatially and is partially influenced by natural and anthropogenic physiographic features
Dynamic rating curves exhibit cyclic behavior at various timescales across the US, which is also partially explained by basin features
Warm, moist, and longitudinally confined tropical air masses are being linked to some of the most extreme precipitation and flooding events in the midlatitudes. The interannual frequency and ...intensity of such atmospheric rivers (ARs), or tropical moisture exports (TMEs), are connected to the risk of extreme precipitation events in areas where moisture convergence occurs. This study presents a nonstationary, regional frequency analysis of precipitation extremes in Northern California that is conditioned on the interannual variability of TMEs entering the region. Parameters of a multisite peaks‐over‐threshold model are allowed to vary conditional on the integrated moisture delivery from TMEs over the area. Parameters are also related to time‐invariant, local characteristics to facilitate regionalization to ungaged sites. The model is developed and calibrated in a hierarchical Bayesian framework to support partial pooling and enhance regionalization skill. The model is cross validated along with two alternative, increasingly parsimonious formulations to assess the additional skill provided by the covariates. Climate diagnostics are also used to better understand the instances where TMEs fail to explain variations in rainfall extremes to provide a path forward for further model improvement. The modeling structure is designed to link seasonal forecasting and long‐term projections of TMEs directly to regional models of extremes used for risk estimation. Results suggest that the inclusion of TME‐based information greatly improves the characterization of extremes, particularly for their frequency of occurrence. Diagnostics indicate that the model could be further improved by considering an index for frontal systems as an additional covariate.
Key Points:
Tropical moisture exports modulate the risk of extremes
A regional POT model is improved using tropical moisture export information
The POT model could be further improved by considering frontal systems
This study examines the hydrologic and climatic conditions that precede major flood events on Lake Ontario, with the purpose of understanding the potential for seasonal forecasts to inform lake level ...management. Seven late spring/early summer flood events are identified since 1949, including the record‐breaking flood of 2017. The surface climate, atmospheric circulation, and antecedent lake levels for the preceding winter and spring seasons are examined. Results suggest that flood events are caused by different combinations of high, initial wintertime water levels across all of the Great Lakes, anomalously wet winters across the entire Great Lakes basin, and wet spring conditions, particularly in the eastern part of the basin. Wet winters that precede flood events are often associated with La Niña conditions, while wet springs are often associated with a westward shift of the North Atlantic Subtropical High. As the critical drawdown period for Lake Ontario occurs in the fall, before the onset of anomalous winter or spring/summer inputs, a generalized additive model was used to predict April–August maximum monthly average Lake Ontario water levels using November levels for all Great Lakes, a nonlinear response to the wintertime Niño 3.4 index, and scenarios of April–May overbasin precipitation. The Niño 3.4 index significantly improves lake level predictions, suggesting that an El Niño‐Southern Oscillation signal may be useful for lake level management. Future work needed to verify the use of El Niño‐Southern Oscillation for Lake Ontario flood forecasting and to link the North Atlantic Subtropical High to predictions of springtime Great Lakes climate is discussed.
Key Points
Lake Ontario floods are caused by high antecedent water levels in all of the Great Lakes and anomalous winter and spring precipitation
ENSO and the North Atlantic Subtropical High are drivers of winter/spring hydroclimatological extremes impacting Lake Ontario
ENSO can inform hydroclimatic forecasts at critical time scales for lake level management
This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) ...precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.
The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. ...A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: While historical and paleo climate records emphasize low‐frequency modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi‐periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N‐year historical record. The uncertainty of IPCC‐like future scenarios is considered through an equivalent sample size N. The relative importance of estimating short‐ or long‐term risk depends on the investment life M. Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation.
Key Points
Quasi‐periodic and secular climate signals, with different identifiability and predictability, control future uncertainty and risk
Adaptation strategies must consider how uncertainties in risk projections influence success of decision pathways
Stylized experiments reveal how bias and variance of climate risk projections influence risk mitigation over a finite planning period
Abstract
This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate ...projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
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