scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2020 ◽  
Vol 24 (5) ◽  
pp. 2817-2839
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools (last access: 19 May 2020).

scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2019 ◽  
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems and the population’s traditional livelihoods. In the Lena River catchment, Eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We focus on the Lena River catchment, where significant environmental changes are already apparent. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scale. The method yields dissimilarities or emergence levels ranging from 0 to 100 % and the direction of change as continuous time series itself. For the Lena River catchment, on average, emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools.

Evaluation of a novel non-parametric approach to identify Time of Emergence (ToE) of climate signals

2020 ◽  
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Natural Variability ◽  
Sensitivity Analyses ◽  
Arbitrary Parameter ◽  
High Temporal Resolution ◽  
Density Estimator ◽  
Reference Period ◽  
Climate Signal ◽  
Significance Level

<p>The time when a climate signal permanently exceeds its natural variability is called time of emergence (ToE). ToE shall serve policy makers as an indication of when to expect the climate and the environment to undergo significant changes. Identifying ToE, however, is challenging, primarily because of the lack of a standard to quantify exceedance, which, in turn, requires a definition of a natural background variability. Existing approaches often rely on a high level of arbitrary parameter values, e.g. selecting a specific number of times the standard deviation of a reference period as natural variability, selecting specific moving window widths to smooth a signal, or the arbitrary choice of a significance level for a statistical test. Such choices of course have a large influence on the final results and would in theory require exhaustive sensitivity analyses and discussion.</p><p>In order to minimize the level of parameterization for ToE estimates, we have developed a novel approach. It assesses exceedance of a climate signal by measuring distances between probability density functions (PDF) of the signal at different times (reference vs. target periods), using the Hellinger distance (HD) metric. The HD metric can be understood as the geometrical overlap of the respective PDFs and we adjusted it to describe the emergence as dissimilarity (0%-100%). In order to derive the PDFs, we use a kernel density estimator (KDE). This, however, introduces the KDE-bandwidth hyperparameter, which determines how smoothly the PDF is generated. Together with the choices for the length of the target and reference periods, and the end of the reference period, a set of less numerous but unavoidable hyperparameters are present that affect the outcome of ToE estimates. We present an extensive sensitivity analysis and highlight strengths and shortcomings of our approach with respect to the frequently used Kolmogorov–Smirnov (KS) test, and the used distance metric within it. We consider a set of synthetic datasets that show similar features as climate model temperature time series. In these datasets, we control the onset of change, variability levels, or trends in the data. Results show that our approach can more precisely identify the changes as compared to the KS-based approach. In particular when the changes in the signal are of low amplitude and sample sizes are small, our approach performs superior. The sensitivity of our approach in the considered tests to varying KDE-bandwidths is less than 5%. The approach has so far been applied on time-series of annual temperature and precipitation. Changes in the distribution of various other climate variables are potential fields of application. Associated challenges with non normally-distributed data, for example high temporal resolution precipitation data, are discussed.</p>

scholarly journals Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for regional climate scenarios: a case study for West-Central Europe

2005 ◽  
Vol 5 (4) ◽  
pp. 7415-7455 ◽  
Author(s):  
A. P. van Ulden ◽  
G. J. van Oldenborgh
Keyword(s):  
Climate Change ◽  
20Th Century ◽  
Central Europe ◽  
21St Century ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Scenarios ◽  
Model Simulations ◽  
Circulation Changes

Abstract. The credibility of regional climate change predictions for the 21st century depends on the ability of climate models to simulate global and regional circulations in a realistic manner. To investigate this issue, a large set of global coupled climate model experiments prepared for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change has been studied. First we compared 20th century model simulations of longterm mean monthly sea level pressure patterns with ERA-40. We found a wide range in performance. Many models performed well on a global scale. For northern midlatitudes and Europe many models showed large errors, while other models simulated realistic pressure fields. Next we focused on the monthly mean climate of West-Central Europe in the 20th century. In this region the climate depends strongly on the circulation. Westerlies bring temperate weather from the Atlantic Ocean, while easterlies bring cold spells in winter and hot weather in summer. In order to be credible for this region, a climate model has to show realistic circulation statistics in the current climate, and a response of temperature and precipitation variations to circulation variations that agrees with observations. We found that even models with a realistic mean pressure pattern over Europe still showed pronounced deviations from the observed circulation distributions. In particular, the frequency distributions of the strength of westerlies appears to be difficult to simulate well. This contributes substantially to biases in simulated temperatures and precipitation, which have to be accounted for when comparing model simulations with observations. Finally we considered changes in climate simulations between the end of the 20th century and the end of the 21st century. Here we found that changes in simulated circulation statistics play an important role in climate scenarios. For temperature, the warm extremes in summer and cold extremes in winter are most sensitive to changes in circulation, because these extremes depend strongly on the simulated frequency of eastery flow. For precipitation, we found that circulation changes have a substantial influence, both on mean changes and on changes in the probability of wet extremes and of long dry spells. Because we do not know how reliable climate models are in their predictions of circulation changes, climate change predictions for Europe are as yet uncertain in many aspects.

scholarly journals The Impact of Different Absolute Solar Irradiance Values on Current Climate Model Simulations

2014 ◽  
Vol 27 (3) ◽  
pp. 1100-1120 ◽  
Author(s):  
David H. Rind ◽  
Judith L. Lean ◽  
Jeffrey Jonas
Keyword(s):  
Climate Change ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Global Climate ◽  
Stratospheric Ozone ◽  
Model Simulations ◽  
The Impact

Abstract Simulations of the preindustrial and doubled CO2 climates are made with the GISS Global Climate Middle Atmosphere Model 3 using two different estimates of the absolute solar irradiance value: a higher value measured by solar radiometers in the 1990s and a lower value measured recently by the Solar Radiation and Climate Experiment. Each of the model simulations is adjusted to achieve global energy balance; without this adjustment the difference in irradiance produces a global temperature change of 0.4°C, comparable to the cooling estimated for the Maunder Minimum. The results indicate that by altering cloud cover the model properly compensates for the different absolute solar irradiance values on a global level when simulating both preindustrial and doubled CO2 climates. On a regional level, the preindustrial climate simulations and the patterns of change with doubled CO2 concentrations are again remarkably similar, but there are some differences. Using a higher absolute solar irradiance value and the requisite cloud cover affects the model’s depictions of high-latitude surface air temperature, sea level pressure, and stratospheric ozone, as well as tropical precipitation. In the climate change experiments it leads to an underestimation of North Atlantic warming, reduced precipitation in the tropical western Pacific, and smaller total ozone growth at high northern latitudes. Although significant, these differences are typically modest compared with the magnitude of the regional changes expected for doubled greenhouse gas concentrations. Nevertheless, the model simulations demonstrate that achieving the highest possible fidelity when simulating regional climate change requires that climate models use as input the most accurate (lower) solar irradiance value.

scholarly journals The representation of location by regional climate models in complex terrain

2015 ◽  
Vol 12 (3) ◽  
pp. 3011-3028 ◽  
Author(s):  
D. Maraun ◽  
M. Widmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Signal ◽  
Model Errors ◽  
Model Simulations ◽  
Local Grid ◽  
Climate Model Simulations

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

scholarly journals Development of probability distributions for regional climate change from uncertain global mean warming and an uncertain scaling relationship

2007 ◽  
Vol 11 (3) ◽  
pp. 1097-1114 ◽  
Author(s):  
B. Hingray ◽  
A. Mezghani ◽  
T. A. Buishand
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Probability Distributions ◽  
Regional Climate ◽  
Natural Variability ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
Global Mean

Abstract. To produce probability distributions for regional climate change in surface temperature and precipitation, a probability distribution for global mean temperature increase has been combined with the probability distributions for the appropriate scaling variables, i.e. the changes in regional temperature/precipitation per degree global mean warming. Each scaling variable is assumed to be normally distributed. The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted within the framework of the EU-funded project PRUDENCE, using an Analysis Of Variance (ANOVA). For the area covered in the 2001–2004 EU-funded project SWURVE, five case study regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). The resulting regional climate changes for 2070–2099 vary quite significantly between CSRs, between seasons and between meteorological variables. For all CSRs, the expected warming in summer is higher than that expected for the other seasons. This summer warming is accompanied by a large decrease in precipitation. The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer because of the differences between regional climate models. Differences between the spatial climate-change patterns of global climate model simulations make significant contributions to the uncertainty of the scaling ratio for temperature. However, no meaningful contribution could be found for the scaling ratio for precipitation due to the small number of global climate models in the PRUDENCE project and natural variability, which is often the largest source of uncertainty. In contrast, for temperature, the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. For the regional change in precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.

Evaluation of climate model simulations in representing the precipitation non-stationarity by considering observational uncertainties

2021 ◽  
Author(s):  
Yongjing Wan ◽  
Jie Chen ◽  
Ping Xie ◽  
Chong-Yu Xu ◽  
Daiyuan Li
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Annual Precipitation ◽  
Future Climate Change ◽  
Spatial Distributions ◽  
Model Simulations ◽  
The Mean ◽  
Climate Model Simulations

<p>The reliability of climate model simulations in representing the precipitation changes is one of the preconditions for climate-change impact studies. However, the observational uncertainties hinder the robust evaluation of these climate model simulations. The goal of the present study is to evaluate the capacities of climate model simulations in representing the precipitation non-stationarity in consideration of observational uncertainties. The mean of multiple observations (OBSE) from five observational precipitation datasets is used as a reference to quantify the uncertainty of observed precipitation and to evaluate the performance of climate model simulations. The non-stationarity of precipitation was represented using the mean and variance of annual total precipitation and annual maximum daily precipitation for the 1982–2015 period. The results show that the spatial distributions of annual and extreme precipitation are similar for various observational datasets, while there has less agreement in the variance changes of extreme precipitation. Climate models are capable of representing the spatial distributions of the annual and extreme precipitation amounts at the global scales. In terms of the non-stationarity, climate model simulations are capable of capturing the large-scale spatial pattern of the trend in mean for annual precipitation. On the contrary, the simulations are less reliable in reproducing the change of extreme precipitation, as well as the trend of variance for annual precipitation. Overall, climate models are more reliable in simulating the mean of precipitation than the variance, and they are more reliable in simulating annual precipitation than extreme. Besides, the uncertainties of precipitation for both observations and simulations are much larger in monsoon regions than in other regions. This study suggests that considering observational uncertainties is necessary when using observational datasets as the reference to project future climate change and assess the impact of climate change on environments.</p>

Assessing maximum potential water withdrawal for food production under climate change - an application in Spain

2014 ◽  
Vol 5 (4) ◽  
pp. 633-651
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Water Withdrawal ◽  
Climate Change Projections ◽  
Adaptation Measures ◽  
Monthly Time Series

This paper provides and tests a methodology to compute surface water (SW) availability for irrigation on regulated systems at large scale, considering different alternatives of streamflow monthly time series derived from regional climate models. SW availability for consumptive use for a river basin is estimated through the concept of maximum potential water withdrawal (MPWW). MPWW is defined as the maximum demand that can be supplied at a given point in the river network under certain conditions: management restrictions (such as ecological flows), demand priorities, monthly distribution of demand and required reliability. Calculation was applied in 567 basins that cover the entirety of mainland Spain to evaluate adaptation needs for agriculture by comparing MPWW for irrigation in the current situation and under climate change projections. The results show that streamflow monthly time series obtained from the regional climate model simulations and bias corrected by University of New Hampshire/Global Runoff Data Centre (UNH/GRDC) dataset and Schreiber's formula provide MPWW values similar to those obtained with the observed data under current situations. Under climate change projections, the capability to satisfy water requirements for agricultural production is significantly reduced and adaptation measures are necessary to mitigate the expected long-term impact.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

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