Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

Jiang, Mingkai; Felzer, B. S.; Sahagian, Dork

doi:10.1038/srep29962

Cited by 16 publications

(13 citation statements)

References 45 publications

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“…In one regime, the total variance is dominated by the monthly means (seasonal component) while the other regime is dominated by the random (residual) component. This separation shows good agreement with previous studies based on different approaches that investigate the predictability of precipitation (Jiang et al, 2016 and. In particular, those regions with a high predictability of precipitation also have a high fraction of the total variance that is due to the seasonal component.…”

Section: Discussionsupporting

confidence: 90%

Technical note: Decomposing a time series into independent trend, seasonal and random components

Yin

Slatford

Roderick

2018

Preprint

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Abstract. Many time series observations in hydrology and climate show large seasonal variations and it has long been common practice to separate the original data into trend, seasonal and random components. We were interested in using that decomposition approach as a basis for understanding variability in hydro-climatic time series. For that purpose, it is desirable that the trend, seasonal and random components are independent so that the variance of the original time series equals the sum of the variances of the three components. We show that the resulting decomposition with the trend component traditionally estimated either as a linear trend or a moving average does not produce components that are independent. Instead we introduce the rarely adopted two-way ANOVA model into studies of hydro-climatic variability and define the trend as equal to the annual anomaly. This traditional approach produces a decomposition with three independent components. We then use global land precipitation data to demonstrate a simple application showing how this decomposition method can be used as a basis for comparing hydro-climatic variability. We anticipate that the three-part decomposition based on the two-way ANOVA approach will prove useful for future applications that seek to understand the space-time dimensions of hydro-climatic variability.

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Section: Discussionsupporting

confidence: 90%

Technical note: Decomposing a time series into independent trend, seasonal and random components

Yin

Slatford

Roderick

2018

Preprint

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“…We assumed that the other values used in the Fridley models would not change in the two future time periods. Precipitation in the southern Appalachians is not predicted to change drastically in the near future (Jiang et al 2016), and other values, including radiative heating, stream location and topographic convergence index, are dependent on the topology and location, which should remain relatively constant.…”

Section: Ground Temperaturementioning

confidence: 99%

Vanishing islands in the sky? A comparison of correlation‐ and mechanism‐based forecasts of range dynamics for montane salamanders under climate change

Lyons

Kozak

2019

Ecography

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Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species‐specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range‐limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi‐model ensemble averages. Our results indicate that correlative models are over‐predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.

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“…Seasonal variability of the annual maximum daily precipitation was analyzed based on Colwell indices of predictability P and its components, i.e., constancy C and seasonality M. These indices are commonly used in hydrology to evaluate the variability of flows, precipitation, and seasonality of their occurrence [37,38]. Moreover, it should be emphasized that the literature has not provided a lot of results so far since Colwell indices were used to analyze the seasonality of annual maximum daily precipitation (only other types of precipitation were analyzed).…”

Section: Seasonality Analysis Of the Occurrence Of The Annual Maximummentioning

confidence: 99%

Trends, Variability, and Seasonality of Maximum Annual Daily Precipitation in the Upper Vistula Basin, Poland

2018

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The aim of this study was to detect trends in maximum annual daily precipitation in the Upper Vistula Basin. We analyzed data from 51 weather stations between 1971 and 2014. Then we used the Mann–Kendall test to detect monotonical trends of the precipitation for three significance levels: 1, 5, and 10%. Our analysis of weather conditions helped us describe the mechanism behind the formation of maximum annual daily precipitation. To analyze precipitation seasonality, we also used Colwell indices. Our study identified a significant trend of the highest daily precipitation for the assumed significance levels (0.01, 0.05, 0.1) for 22% of the investigated weather stations at different elevations. The significant trends found were positive and an increase in precipitation is expected. From 1971 to 2014, the maximum daily total precipitation most often occurred in the summer half-year, i.e., from May until September. These months included a total of 88% of days with the highest daily precipitation. The predictability index for the highest total precipitation within the area was high and exceeded 5%. It was markedly affected by the coefficient of constancy (C) and to a lesser degree by the seasonality index (M). Our analysis demonstrated a convergence of the Colwell indices and frequency of cyclonic situation and, therefore, confirmed their usability in the analysis of precipitation seasonality.

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Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

Cited by 16 publications

References 45 publications

Technical note: Decomposing a time series into independent trend, seasonal and random components

Technical note: Decomposing a time series into independent trend, seasonal and random components

Vanishing islands in the sky? A comparison of correlation‐ and mechanism‐based forecasts of range dynamics for montane salamanders under climate change

Trends, Variability, and Seasonality of Maximum Annual Daily Precipitation in the Upper Vistula Basin, Poland

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