A Stochastic Rainfall Generator Approach for Applying Downscaled Global Climate Model Projections to Local Precipitation Time Series Data in Philadelphia

Abstract The temperature of the stratosphere has decreased over the past several decades. Two causes contribute to that decrease: well-mixed greenhouse gases (GHGs) and ozone-depleting substances (ODSs). This paper addresses the attribution of temperature decreases to these two causes and the implications of that attribution for the future evolution of stratospheric temperature. Time series analysis is applied to simulations of the Goddard Earth Observing System Chemistry–Climate Model (GEOS CCM) to separate the contributions of GHGs from those of ODSs based on their different time-dependent signatures. The analysis indicates that about 60%–70% of the temperature decrease of the past two decades in the upper stratosphere near 1 hPa and in the lower midlatitude stratosphere near 50 hPa resulted from changes attributable to ODSs, primarily through their impact on ozone. As ozone recovers over the next several decades, the temperature should continue to decrease in the middle and upper stratosphere because of GHG increases. The time series of observed temperature in the upper stratosphere is approaching the length needed to separate the effects of ozone-depleting substances from those of greenhouse gases using temperature time series data.

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Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.692.97 ◽

2014 ◽

Vol 692 ◽

pp. 97-102 ◽

Cited By ~ 1

Author(s):

Ijaz Ahmad ◽

De Shan Tang ◽

Mei Wang ◽

Sarfraz Hashim

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Positive Trend ◽

Precipitation Time Series ◽

Significance Level ◽

Annual Basis ◽

Time Period ◽

Seasonal Basis ◽

Correlation Trend

This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh station exhibits the most number of significant cases on the seasonal basis while, no significant trends are found at Thalozom, Kalam and Dir stations. On the annual basis, only Charbagh station shows a significant positive trend, while on other stations, no significant trends are found annually. The performance of MK and SR tests was consistent in detecting the trend at different stations.

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Empirical estimate of the signal content of Holocene temperature proxy records

10.5194/cp-2018-154 ◽

2018 ◽

Author(s):

Maria Reschke ◽

Kira Rehfeld ◽

Thomas Laepple

Keyword(s):

Time Series ◽

Climate Models ◽

Climate Model ◽

Time Series Data ◽

Series Data ◽

Past Climate ◽

Proxy Records ◽

Model Time Series ◽

Climate Model Simulations ◽

Temperature Proxy

Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate related effects as well as time uncertainty. As proxy based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy records’ signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the mid to late Holocene. This is achieved through a comparison of proxy time series from close-by sites of three compilations and model time series data at the proxy sites from two transient Holocene climate model simulations. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial time scales to be rather low (R

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Robust assessment of future changes in extreme precipitation over the Rhine basin using a GCM

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-9043-2010 ◽

2010 ◽

Vol 7 (6) ◽

pp. 9043-9066 ◽

Cited By ~ 2

Author(s):

S. F. Kew ◽

F. M. Selten ◽

G. Lenderink ◽

W. Hazeleger

Keyword(s):

Time Series ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Large River ◽

Ensemble Size ◽

Rhine Basin ◽

Scaling Methods ◽

Long Time ◽

Simple Scaling

Abstract. Estimates of future changes in extremes of multiday precipitation sums are critical for estimates of future discharge extremes of large river basins. Here we use a large ensemble of global climate model SRES A1b scenario simulations to estimate changes in extremes of 1–20 day precipitation sums over the Rhine basin, projected for the period 2071–2100 with reference to 1961–1990. We find that in winter, an increase of order 10%, for the 99th percentile precipitation sum, is approximately fixed across the selected range of multiday sums, whereas in summer, the changes become increasingly negative as the summation time lengthens. Explanations for these results are presented that have implications for simple scaling methods for creating time series of a future climate. We show that this scaling behavior is sensitive to the ensemble size and indicate that currently available discharge estimates from previous studies are based on insufficiently long time series.

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Use of Integrated Global Climate Model Simulations and Statistical Time Series Forecasting to Project Regional Temperature and Precipitation

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0204.1 ◽

2021 ◽

Author(s):

Yuchuan Lai ◽

David A. Dzombak

Keyword(s):

Climate Change ◽

Time Series ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Arima Model ◽

Climate Change Signal ◽

Temperature And Precipitation ◽

Regional Temperature ◽

Statistical Time

AbstractAn integrated technique combining global climate model (GCM) simulation results and a statistical time series forecasting model (the autoregressive integrated moving average ARIMA model) was developed to bring together the climate change signal from GCMs to city-level historical observations as an approach to obtain location-specific temperature and precipitation projections. This approach assumes that regional temperature and precipitation time series reflect a combination of an underlying climate change signal series and a regional-deviation-from-the-signal series. An ensemble of GCMs is used to describe and provide the climate change signal, and the ARIMA model is used to model and project the regional deviation. Qualitative and quantitative assessments were conducted for evaluating the projection performance of the hybrid GCM-ARIMA (G-ARIMA) model. The results indicate that the G-ARIMA model can provide projected city-specific daily temperature and precipitation series comparable to historical observations and can have improved projection accuracy for several assessed annual indices compared to a commonly used downscaled projection product. The G-ARIMA model is subject to some limitations and uncertainties from the GCM-provided climate change signal. A notable feature of the G-ARIMA model is the efficiency with which projections can be updated when new observations become available, thus facilitating updating of regional temperature and precipitations projections. Given the increasing need for and use of location-specific climate projections in practical engineering applications, the G-ARIMA model is an option for regional temperature and precipitation projection for such applications.

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Similarities within a multi-model ensemble: functional data analysis framework

Geoscientific Model Development ◽

10.5194/gmd-12-735-2019 ◽

2019 ◽

Vol 12 (2) ◽

pp. 735-747 ◽

Cited By ~ 3

Author(s):

Eva Holtanová ◽

Thomas Mendlik ◽

Jan Koláček ◽

Ivanka Horová ◽

Jiří Mikšovský

Keyword(s):

Time Series ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Future Climate Change ◽

Model Ensembles

Abstract. Despite the abundance of available global climate model (GCM) and regional climate model (RCM) outputs, their use for evaluation of past and future climate change is often complicated by substantial differences between individual simulations and the resulting uncertainties. In this study, we present a methodological framework for the analysis of multi-model ensembles based on a functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behavior of entire simulated climatic time series, encompassing both past and future periods, is introduced. To our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series, we used two semimetrics based on Euclidean distances between the simulated trajectories and based on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs, the data are ordered on a two-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles (BI) and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX RCMs and their driving GCMs over the 1971–2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed.

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Comparison of Gridded Precipitation Time Series Data in APHRODITE and Asfazari Databases within Iran’s Territory

Atmospheric and Climate Sciences ◽

10.4236/acs.2013.32025 ◽

2013 ◽

Vol 03 (02) ◽

pp. 235-248 ◽

Cited By ~ 5

Author(s):

Esmail Nasrabadi ◽

Seyyed Abolfazl Masoodian ◽

Hossein Asakereh

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Precipitation Time Series

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Dynamic carbon flux network of a diverse marine microbial community

ISME Communications ◽

10.1038/s43705-021-00055-7 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Marvin M. Mayerhofer ◽

Falk Eigemann ◽

Carsten Lackner ◽

Jutta Hoffmann ◽

Ferdi L. Hellweger

Keyword(s):

Time Series ◽

Carbon Flux ◽

Large Scale ◽

Time Series Data ◽

Heterotrophic Bacteria ◽

Global Climate ◽

Mechanistic Model ◽

System Level ◽

Series Data ◽

Microbial Ecosystems

AbstractThe functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton—heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.

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