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1. A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States

   https://doi.org/10.1029/2018WR024446  

   Steinschneider, Scott ; Ray, Patrick ; Rahat, Saiful Haque ; Kucharski, John ( July 2019 , Water resources research)

   Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negativelymore »biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.« less
2. Subseasonal convection variability over the Intra‐American Seas simulated by an AGCM and sensitivity to CMIP5 SST biases and projections

   https://doi.org/https://doi.org/10.1002/joc.6475  

   Vigaud, N ( January 2020 , International journal of climatology)

   The influence of coupled model sea surface temperature (SST) climatological biases and SST projections on daily convection over the Intra-American Seas (IAS) during the May–November rainy season are examined by clustering (k − means) daily OLR anomalies in ECHAM5 atmospheric global climate model (AGCM) experiments. The AGCM is first forced by 1980–2005 observed SSTs (GOGA), then with climatological, multi-model mean monthly climatological SST bias from 31 CMIP5 coupled models (HIST) and projected SST changes for 2040–2059 (PROJ) and 2080–2099 (PROJ2) imposed on top of observed values. A typology of seven recurrent convection regimes is identified and consists of three dry and four wet regimes, including three regimes characterized by tropical-midlatitude interactions between surface convection cells across the IAS and Rossby wave in the upper-troposphere, and a regime of broad wettening typical of the ITCZ. Compared to an earlier observational study, all seven regimes are reasonably well reproduced in the HIST runs. However, the latter exhibit drier dry regimes, a less wet ITCZ-like wet regime and a southeastward shift of convective anomalies developing across the IAS in the three other regimes, all result in a drier simulated IAS climate compared to GOGA. ECHAM5 projection runs for PROJ and PROJ2 are bothmore »characterized by the inhibition of the broad ITCZ-like wet regime, indicating a significant trend towards more frequent dry weather. Meanwhile, convection anomalies related to tropical-midlatitude interactions are shifted further east of the Caribbean as lead increases. These results suggest more frequent and intense extreme rainfall over the tropical Atlantic and the southeast US, while parts of the Caribbean are projected to experience drier climate. The projected drying, however, is of the same order of magnitude as results from historical SST biases, suggesting that the latter need to be considered in model projections, which might underestimate future IAS drying.« less
3. Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

   https://doi.org/10.5194/hess-24-2253-2020  

   Feng, Dongmei ; Beighley, Edward ( January 2020 , Hydrology and Earth System Sciences)

   Abstract. Assessing impacts of climate change on hydrologic systemsis critical for developing adaptation and mitigation strategies for waterresource management, risk control, and ecosystem conservation practices. Suchassessments are commonly accomplished using outputs from a hydrologic modelforced with future precipitation and temperature projections. The algorithmsused for the hydrologic model components (e.g., runoff generation) canintroduce significant uncertainties into the simulated hydrologic variables.Here, a modeling framework was developed that integrates multiple runoffgeneration algorithms with a routing model and associated parameteroptimizations. This framework is able to identify uncertainties from bothhydrologic model components and climate forcings as well as associatedparameterization. Three fundamentally different runoff generationapproaches, runoff coefficient method (RCM, conceptual), variableinfiltration capacity (VIC, physically based, infiltration excess), andsimple-TOPMODEL (STP, physically based, saturation excess), were coupledwith the Hillslope River Routing model to simulate surface/subsurface runoffand streamflow. A case study conducted in Santa Barbara County, California,reveals increased surface runoff in February and March but decreasedrunoff in other months, a delayed (3 d, median) and shortened (6 d,median) wet season, and increased daily discharge especially for theextremes (e.g., 100-year flood discharge, Q100). The Bayesian modelaveraging analysis indicates that the probability of such an increase can be up to85 %. For projected changes in runoff and discharge, general circulationmodels (GCMs) and emission scenariosmore »are two major uncertainty sources,accounting for about half of the total uncertainty. For the changes inseasonality, GCMs and hydrologic models are two major uncertaintycontributors (∼35 %). In contrast, the contribution ofhydrologic model parameters to the total uncertainty of changes in thesehydrologic variables is relatively small (<6 %), limiting theimpacts of hydrologic model parameter equifinality in climate change impactanalysis. This study provides useful information for practices associatedwith water resources, risk control, and ecosystem conservation and forstudies related to hydrologic model evaluation and climate change impactanalysis for the study region as well as other Mediterranean regions.« less
4. Impact of the North Atlantic Warming Hole on Sensible Weather

   https://doi.org/10.1175/JCLI-D-19-0636.1  

   Gervais, Melissa ; Shaman, Jeffrey ; Kushnir, Yochanan ( May 2020 , Journal of Climate)

   In future climate projections there is a notable lack of warming in the North Atlantic subpolar gyre, known as the North Atlantic warming hole (NAWH). In a set of large-ensemble atmospheric simulations with the Community Earth System Model, the NAWH was previously shown to contribute to the projected poleward shift and eastward elongation of the North Atlantic jet. The current study investigates the impact of the warming hole on sensible weather, particularly over Europe, using the same simulations. North Atlantic jet regimes are classified within the model simulations by applying self-organizing maps analysis to winter daily wind speeds on the dynamic tropopause. The NAWH is found to increase the prevalence of jet regimes with stronger and more-poleward-shifted jets. A previously identified transient eddy-mean response to the NAWH that leads to a downstream enhancement of wind speeds is found to be dependent on the jet regime. These localized regime-specific changes vary by latitude and strength, combining to form the broad increase in seasonal-mean wind speeds over Eurasia. Impacts on surface temperature and precipitation within the various North Atlantic jet regimes are also investigated. A large decrease in surface temperature over Eurasia is found to be associated with the NAWH in regimesmore »where air masses are advected eastward over the subpolar gyre prior to reaching Eurasia. Precipitation is found to be locally suppressed over the warming hole region and increased directly downstream. The impact of this downstream response on coastal European precipitation is dependent on the strength of the NAWH.

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5. A Nonstationary Standardized Precipitation Index (NSPI) Using Bayesian Splines

   https://doi.org/10.1175/JAMC-D-21-0244.1  

   Stagge, James H. ; Sung, Kyungmin ( July 2022 , Journal of Applied Meteorology and Climatology)

   The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an applicationmore »of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.

   We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.

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