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 negatively 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.
Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs.
- Award ID(s):
- 1702273
- NSF-PAR ID:
- 10453469
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- International Journal of Climatology
- Volume:
- 41
- Issue:
- 4
- ISSN:
- 0899-8418
- Page Range / eLocation ID:
- p. 2456-2479
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Future Arctic sea ice loss has a known impact on Arctic amplification (AA) and mean atmospheric circulation. Furthermore, several studies have shown it leads to a decreased variance in temperature over North America. In this study, we analyze results from two fully coupled Community Earth System Model (CESM) Whole Atmosphere Community Climate Model (WACCM4) simulations with sea ice nudged to either the ensemble mean of WACCM historical runs averaged over the 1980–99 period for the control (CTL) or projected RCP8.5 values over the 2080–99 period for the experiment (EXP). Dominant large-scale meteorological patterns (LSMPs) are then identified using self-organizing maps applied to winter daily 500-hPa geopotential height anomalies (
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The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.more » « less
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Abstract Observational evidence shows changes to North American weather regime occurrence depending on the strength of the lower-stratospheric polar vortex. However, it is not yet clear how this occurs or to what extent an improved stratospheric forecast would change regime predictions. Here we analyze four North American regimes at 500 hPa, constructed in principal component (PC) space. We consider both the location of the regimes in PC space and the linear regression between each PC and the lower-stratospheric zonal-mean winds, yielding a theory of which regime transitions are likely to occur due to changes in the lower stratosphere. Using a set of OpenIFS simulations, we then test the effect of relaxing the polar stratosphere to ERA-Interim on subseasonal regime predictions. The model start dates are selected based on particularly poor subseasonal regime predictions in the European Centre for Medium-Range Weather Forecasts CY43R3 hindcasts. While the results show only a modest improvement to the number of accurate regime predictions, there is a substantial reduction in Euclidean distance error in PC space. The average movement of the forecasts within PC space is found to be consistent with expectation for moderate-to-large lower-stratospheric zonal wind perturbations. Overall, our results provide a framework for interpreting the stratospheric influence on North American regime behavior. The results can be applied to subseasonal forecasts to understand how stratospheric uncertainty may affect regime predictions, and to diagnose which regime forecast errors are likely to be related to stratospheric errors. Significance Statement Predicting the weather several weeks ahead is a major challenge with large potential benefits to society. The strength of the circulation more than 10 km above the Arctic during winter (i.e., the polar vortex) is one source of predictability. This study investigates how forecast error and uncertainty in the polar vortex can impact predictions of large-scale weather patterns called “regimes” over North America. Through statistical analysis of observations and experiments with a weather forecast model, we develop an understanding of which regime changes are more likely to be due to changes in the polar vortex. The results will help forecasters and researchers understand the contribution of the stratosphere to changes in weather patterns, and in assessing and improving weather forecast models.more » « less
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Abstract Weather regimes defined through cluster analysis concisely categorize the anomalous regional circulation pattern on any given day. Owing to their persistence and low dimensionality, regimes are increasingly used in subseasonal-to-seasonal prediction and in analysis of climate variability and change. However, a limitation of existing regime classifications for North America is their seasonal dependence, with most existing studies defining regimes for winter only. Here, we normalize the seasonal cycle in daily geopotential height variance and use empirical orthogonal function analysis combined with
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