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 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 Impact of anthropogenic climate change on wildfire across western US forests

2016 ◽  
Vol 113 (42) ◽  
pp. 11770-11775 ◽  
Author(s):  
John T. Abatzoglou ◽  
A. Park Williams
Keyword(s):  
Climate Change ◽  
United States ◽  
Climate Variability ◽  
Forest Fire ◽  
Anthropogenic Climate Change ◽  
Natural Climate Variability ◽  
Western Us ◽  
Fire Activity ◽  
Fire Area ◽  
Natural Climate

Increased forest fire activity across the western continental United States (US) in recent decades has likely been enabled by a number of factors, including the legacy of fire suppression and human settlement, natural climate variability, and human-caused climate change. We use modeled climate projections to estimate the contribution of anthropogenic climate change to observed increases in eight fuel aridity metrics and forest fire area across the western United States. Anthropogenic increases in temperature and vapor pressure deficit significantly enhanced fuel aridity across western US forests over the past several decades and, during 2000–2015, contributed to 75% more forested area experiencing high (>1 σ) fire-season fuel aridity and an average of nine additional days per year of high fire potential. Anthropogenic climate change accounted for ∼55% of observed increases in fuel aridity from 1979 to 2015 across western US forests, highlighting both anthropogenic climate change and natural climate variability as important contributors to increased wildfire potential in recent decades. We estimate that human-caused climate change contributed to an additional 4.2 million ha of forest fire area during 1984–2015, nearly doubling the forest fire area expected in its absence. Natural climate variability will continue to alternate between modulating and compounding anthropogenic increases in fuel aridity, but anthropogenic climate change has emerged as a driver of increased forest fire activity and should continue to do so while fuels are not limiting.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal 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.

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

scholarly journals Rising Variability in Thunderstorm-Related U.S. Losses as a Reflection of Changes in Large-Scale Thunderstorm Forcing*

2013 ◽  
Vol 5 (4) ◽  
pp. 317-331 ◽  
Author(s):  
J. Sander ◽  
J. F. Eichner ◽  
E. Faust ◽  
M. Steuer
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climatic Variability ◽  
The United States ◽  
Reanalysis Data ◽  
Anthropogenic Climate Change ◽  
Natural Climate Variability ◽  
Severe Thunderstorm ◽  
Convective Storms ◽  
Natural Climate

Abstract Thunderstorm-related normalized economic and insured losses in the United States east of the Rockies from the period 1970–2009 (March–September) exhibit higher peaks and greater variability in the last two decades than in the preceding two decades. To remove the bias from increasingly detected losses over time due to newly built-up locations, only large events that incurred normalized losses of at least $250 million (U.S. dollars) economically ($150 million insured) were selected. These are multistate damage events that are unlikely to have been missed at any time within the analysis period, thus providing for homogeneity of the events covered. Those losses, if aggregated, account for the major proportion (~80%) of all thunderstorm-related losses in the period 1970–2009. This study demonstrates that the pattern of variability in the time series of these losses can be seen as a reflection (“fingerprint”) of the temporal variability in severe thunderstorm forcing. The meteorological information on forcing is inferred from NCEP–NCAR reanalysis data. No final attribution of the climatic variability identified in thunderstorm forcing and losses—either to natural climate variability or to anthropogenic climate change—can be conclusively arrived at in this study because of the chosen methodology. Nevertheless, the expected impacts of anthropogenic climate change on the forcing of convective storms appear consistent with these findings.

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