scholarly journals Uncertainty of Hydrological Model Components in Climate Change Studies over Two Nordic Quebec Catchments

2018 ◽  
Vol 19 (1) ◽  
pp. 27-46 ◽  
Author(s):  
Magali Troin ◽  
Richard Arsenault ◽  
Jean-Luc Martel ◽  
François Brissette
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Hydrological Model ◽  
Climate Model ◽  
Global Climate Model ◽  
Substantial Contribution ◽  
Climate Change Signal ◽  
Natural Climate Variability ◽  
Degree Day ◽  
Natural Climate

Abstract Projected climate change effects on hydrology are investigated for the 2041–60 horizon under the A2 emission scenarios using a multimodel approach over two snowmelt-dominated catchments in Canada. An ensemble of 105 members was obtained by combining seven snow models (SMs), five potential evapotranspiration (PET) methods, and three hydrological model (HM) structures. The study was performed using high-resolution simulations from the Canadian Regional Climate Model (CRCM–15 km) driven by two members of the third-generation Canadian Coupled Global Climate Model (CGCM3). This study aims to compare various combinations of SM–PET–HM in terms of their ability to simulate streamflows under the current climate and to evaluate how they affect the assessment of the climate change–induced hydrological impacts at the catchment scale. The variability of streamflow response caused by the use of different SMs (degree-day versus degree-day/energy balance), PET methods (temperature-based versus radiation-based methods), and HM structures is evaluated, as well as the uncertainty due to the natural climate variability (CRCM intermember variability). The hydroclimatic simulations cover 1961–90 in the present period and 2041–60 in the future period. The ensemble spread of the climate change signal on streamflow is large and varies with catchments. Using the variance decomposition on three hydrologic indicators, the HM structure was found to make the most substantial contribution to uncertainty, followed by the choice of the PET methods or natural climate variability, depending on the hydrologic indicator and the catchment. Snow models played a minor, almost negligible role in the assessment of the climate change impacts on streamflow for the study catchments.

scholarly journals Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events

2021 ◽  
Author(s):  
Michael Schirmer ◽  
Adam Winstral ◽  
Tobias Jonas ◽  
Paolo Burlando ◽  
Nadav Peleg
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Variability ◽  
Weather Generator ◽  
Climate Change Signal ◽  
Natural Climate Variability ◽  
Sources Of Uncertainty ◽  
Snow Water ◽  
Snow Model ◽  
Natural Climate

Abstract. Climate projection studies of future changes in snow conditions and resulting rain-on-snow (ROS) flood events are subject to large uncertainties. Typically, emission scenario uncertainties and climate model uncertainties are included. This is the first study on this topic to also include quantification of natural climate variability, which is the dominant uncertainty for precipitation at local scales with large implications for e.g. runoff projections. To quantify natural climate variability, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 × 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The climate change signal for snow water resources stands out as early as mid-century from the noise originating from the three sources of uncertainty investigated, namely uncertainty in emission scenarios, uncertainty in climate models, and natural climate variability. For ROS events, a climate change signal toward more frequent and intense events was found for an RCP 8.5 scenario at high elevations at the end of the century, consistently with other studies. However, for ROS events with a substantial contribution of snowmelt to runoff (>20 %), the climate change signal was largely masked by sources of uncertainty. Only those ROS events where snowmelt does not play an important role during the event will occur considerably more frequently in the future, while ROS events with substantial snowmelt contribution will mainly occur earlier in the year but not more frequently. There are two reasons for this: first, although it will rain more frequently in midwinter, the snowpack will typically still be too cold and dry and thus cannot contribute significantly to runoff; second, the very rapid decline in snowpack toward early summer, when conditions typically prevail for substantial contributions from snowmelt, will result in a large decrease in ROS events at that time of the year. Finally, natural climate variability is the primary source of uncertainty in projections of ROS metrics until the end of the century, contributing more than 70 % of the total uncertainty. These results imply that both the inclusion of natural climate variability and the use of a snow model, which includes a physically-based processes representation of water retention, are important for ROS projections at the local scale.

scholarly journals Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures

2014 ◽  
Vol 18 (6) ◽  
pp. 2033-2047 ◽  
Author(s):  
G. Seiller ◽  
F. Anctil
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Variability ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Hydrological Models ◽  
Natural Climate Variability ◽  
Natural Climate ◽  
Model Structures ◽  
Impacts Of Climate Change

Abstract. Diagnosing the impacts of climate change on water resources is a difficult task pertaining to the uncertainties arising from the different modelling steps. Lumped hydrological model structures contribute to this uncertainty as well as the natural climate variability, illustrated by several members from the same Global Circulation Model. In this paper, the hydroclimatic modelling chain consists of twenty-four potential evapotranspiration formulations, twenty lumped conceptual hydrological models, and seven snowmelt modules. These structures are applied on a natural Canadian sub-catchment to address related uncertainties and compare them to the natural internal variability of simulated climate system as depicted by five climatic members. Uncertainty in simulated streamflow under current and projected climates is assessed. They rely on interannual hydrographs and hydrological indicators analysis. Results show that natural climate variability is the major source of uncertainty, followed by potential evapotranspiration formulations and hydrological models. The selected snowmelt modules, however, do not contribute much to the uncertainty. The analysis also illustrates that the streamflow simulation over the current climate period is already conditioned by the tools' selection. This uncertainty is propagated to reference simulations and future projections, amplified by climatic members. These findings demonstrate the importance of opting for several climatic members to encompass the important uncertainty related to the climate natural variability, but also of selecting multiple modelling tools to provide a trustworthy diagnosis of the impacts of climate change on water resources.

scholarly journals Role of Natural Climate Variability in the Detection of Anthropogenic Climate Change Signal for Mean and Extreme Precipitation at Local and Regional Scales

2018 ◽  
Vol 31 (11) ◽  
pp. 4241-4263 ◽  
Author(s):  
Jean-Luc Martel ◽  
Alain Mailhot ◽  
François Brissette ◽  
Daniel Caya
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Extreme Precipitation ◽  
Local Scale ◽  
Anthropogenic Climate Change ◽  
Climate Change Signal ◽  
Total Precipitation ◽  
Natural Climate Variability ◽  
The World ◽  
Natural Climate

Abstract Climate change will impact both mean and extreme precipitation, having potentially significant consequences on water resources. The implementation of efficient adaptation measures must rely on the development of reliable projections of future precipitation and on the assessment of their related uncertainty. Natural climate variability is a key uncertainty component, which can result in apparent decadal trends that may be greater or lower than the long-term underlying anthropogenic climate change trend. The goal of the present study is to assess how natural climate variability affects the ability to detect the climate change signal for mean and extreme precipitation. Annual and seasonal total precipitation are used as indicators of the mean, whereas annual and seasonal maximum daily precipitation are used as indicators of extremes. This is done using the CanESM2 50-member and CESM1 40-member large ensembles of simulations over the 1950–2100 period. At the local scale, results indicate that natural climate variability will dominate the uncertainty for annual and seasonal extreme precipitation going up to the end of the century in many parts of the world. The climate change signal can, however, be reliably detected much earlier at the regional scale for extreme precipitation. In the case of annual and seasonal total precipitation, the climate change signal can be reliably detected at the local scale without resorting to a regional analysis. Nonetheless, natural climate variability can impede the detection of the anthropogenic climate change signal until the middle to late century in many parts of the world for mean and extreme precipitation.

scholarly journals Recent natural variability in global warming weakened phenological mismatch and selection on seasonal timing in great tits ( Parus major )

2021 ◽  
Vol 288 (1963) ◽  
Author(s):  
Marcel E. Visser ◽  
Melanie Lindner ◽  
Phillip Gienapp ◽  
Matthew C. Long ◽  
Stephanie Jenouvrier
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Variability ◽  
Parus Major ◽  
Great Tit ◽  
Trophic Levels ◽  
Natural Climate Variability ◽  
Seasonal Timing ◽  
Phenological Mismatch ◽  
Natural Climate

Climate change has led to phenological shifts in many species, but with large variation in magnitude among species and trophic levels. The poster child example of the resulting phenological mismatches between the phenology of predators and their prey is the great tit ( Parus major ), where this mismatch led to directional selection for earlier seasonal breeding. Natural climate variability can obscure the impacts of climate change over certain periods, weakening phenological mismatching and selection. Here, we show that selection on seasonal timing indeed weakened significantly over the past two decades as increases in late spring temperatures have slowed down. Consequently, there has been no further advancement in the date of peak caterpillar food abundance, while great tit phenology has continued to advance, thereby weakening the phenological mismatch. We thus show that the relationships between temperature, phenologies of prey and predator, and selection on predator phenology are robust, also in times of a slowdown of warming. Using projected temperatures from a large ensemble of climate simulations that take natural climate variability into account, we show that prey phenology is again projected to advance faster than great tit phenology in the coming decades, and therefore that long-term global warming will intensify phenological mismatches.

scholarly journals Improved Bias Correction Techniques for Hydrological Simulations of Climate Change*

2015 ◽  
Vol 16 (6) ◽  
pp. 2421-2442 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Edwin P. Maurer ◽  
John T. Abatzoglou ◽  
Katherine C. Hegewisch
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate Model ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Signal ◽  
Daily Maximum ◽  
Temperature And Precipitation

Abstract Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once.

scholarly journals Multidecadal ENSO Amplitude Variability in a 1000-yr Simulation of a Coupled Global Climate Model: Implications for Observed ENSO Variability

2013 ◽  
Vol 26 (23) ◽  
pp. 9399-9407 ◽  
Author(s):  
Simon Borlace ◽  
Wenju Cai ◽  
Agus Santoso
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Climate Model ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Warming Trend ◽  
Recent Change ◽  
Climate Change Signal ◽  
Coupled Global Climate Model

The amplitude of the El Niño–Southern Oscillation (ENSO) can vary naturally over multidecadal time scales and can be influenced by climate change. However, determining the mechanism for this variation is difficult because of the paucity of observations over such long time scales. Using a 1000-yr integration of a coupled global climate model and a linear stability analysis, it is demonstrated that multidecadal modulation of ENSO amplitude can be driven by variations in the governing dynamics. In this model, the modulation is controlled by the underlying thermocline feedback mechanism, which in turn is governed by the response of the oceanic thermocline slope across the equatorial Pacific to changes in the overlying basinwide zonal winds. Furthermore, the episodic strengthening and weakening of this coupled interaction is shown to be linked to the slowly varying background climate. In comparison with the model statistics, the recent change of ENSO amplitude in observations appears to be still within the range of natural variability. This is despite the apparent warming trend in the mean climate. Hence, this study suggests that it may be difficult to infer a climate change signal from changes in ENSO amplitude alone, particularly given the presently limited observational data.

Relative impacts of human-induced climate change and natural climate variability

Nature ◽  
10.1038/17789 ◽  
1999 ◽  
Vol 397 (6721) ◽  
pp. 688-691 ◽  
Author(s):  
Mike Hulme ◽  
Elaine M. Barrow ◽  
Nigel W. Arnell ◽  
Paula A. Harrison ◽  
Timothy C. Johns ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Natural Climate Variability ◽  
Natural Climate

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

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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