Spectral representation of the annual cycle in the climate change signal

Bosshard, Thomas; Kotlarski, Sven; Ewen, Tracy; Schär, Christoph

doi:10.5194/hess-15-2777-2011

Cited by 103 publications

(129 citation statements)

References 31 publications

(33 reference statements)

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“…Consequently, it is assumed that the observed biases in the mean and variability of those climate parameters are systematic and will be the same in the future, but it remains to be determined whether the climate model errors are static over time (Maraun et al, 2010). Use of bias correction methods leads to a better fit of the hydrological model output, narrower variability bounds, and improved observed runoff regimes compared to uncorrected climate model data (Bosshard, 2011;Teutschbein and Seibert, 2012). Nevertheless, bias correction can also introduce inconsistency between temperature and precipitation, which strongly affects simulation of snow variables (Dahné et al, 2013) and thereby also influences predictions of future floods.…”

Section: Downscaling and Bias Correctionmentioning

confidence: 99%

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Arheimer

Lindström

2015

Hydrol. Earth Syst. Sci.

130

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Abstract. There is an ongoing discussion whether floods occur more frequently today than in the past, and whether they will increase in number and magnitude in the future. To explore this issue in Sweden, we merged observed time series for the past century from 69 gauging sites throughout the country (450 000 km 2 ) with high-resolution dynamic model projections of the upcoming century. The results show that the changes in annual maximum daily flows in Sweden oscillate between dry and wet periods but exhibit no significant trend over the past 100 years. Temperature was found to be the strongest climate driver of changes in river high flows, which are related primarily to snowmelt in Sweden. Annual daily high flows may decrease by on average −1 % per decade in the future, mainly due to lower peaks from snowmelt in the spring (−2 % per decade) as a result of higher temperatures and a shorter snow season. In contrast, autumn flows may increase by +3 % per decade due to more intense rainfall. This indicates a shift in floodgenerating processes in the future, with greater influence of rain-fed floods. Changes in climate may have a more significant impact on some specific rivers than on the average for the whole country. Our results suggest that the temporal pattern in future daily high flow in some catchments will shift in time, with spring floods in the northern-central part of Sweden occurring about 1 month earlier than today. High flows in the southern part of the country may become more frequent. Moreover, the current boundary between snow-driven floods in northern-central Sweden and rain-driven floods in the south may move toward higher latitudes due to less snow accumulation in the south and at low altitudes. The findings also indicate a tendency in observations toward the modeled projections for timing of daily high flows over the last 25 years. Uncertainties related to both the observed data and the complex model chain of climate impact assessments in hydrology are discussed.

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Section: Downscaling and Bias Correctionmentioning

confidence: 99%

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Arheimer

Lindström

2015

Hydrol. Earth Syst. Sci.

130

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“…The A1B emission scenario represents an evolution close to the median of other storylines and features a rapid economic growth as well as a balanced use of fossil and nonfossil fuels. The changes in air temperature and precipitation used in this study are based on the 'delta change' approach (see e.g., Hay et al, 2000;Salzmann et al, 2007) and have been evaluated by Bosshard et al (2011) for the grid points around Findelengletscher for ten RCMs. The 'delta change' approach expresses the effect of climate change between two periods in terms of the difference in the mean of a given variable.…”

Section: Climate Scenariosmentioning

confidence: 99%

“…We generate transient series of future air temperature and precipitation from the monthly 'delta changes' computed by Bosshard et al (2011) based on RCM results for three 30-year periods in the 21st century. We first interpolate the monthly changes linearly between the center points of the periods (i.e.…”

Section: Daily Meteorological Series Until 2100mentioning

confidence: 99%

“…Scenarios of future climate change are obtained from the project CH2011 (e.g., Bosshard et al, 2011) that presents an analysis of results of the ENSEMBLES climate model runs (van der Linden and Mitchell, 2009). ENSEMBLES RCMs are driven by different GCMs (Table 1) that are forced by the SRES A1B emission scenario (Nakicenovic et al, 2000).…”

Section: Climate Scenariosmentioning

confidence: 99%

“…The calibrated model is driven by changes in air temperature and precipitation given by nine RCMs based on evaluations within the CH2011 project (Bosshard et al, 2011, Table 1). Differences in runoff between the model runs are detached from the glaciohydrological model and depict the a priori uncertainty resulting from the use of different RCM outputs.…”

Section: Experiments I: Climate Scenariomentioning

confidence: 99%

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High uncertainty in 21st century runoff projections from glacierized basins

Huss

Zemp

Joerg

et al. 2014

Journal of Hydrology

102

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Glacier response to a changing climate and its impact on runoff is understood in general terms, but model-based projections are affected by considerable uncertainties. They originate from the driving climate model, input data quality, and simplifications in the glacio-hydrological model and hamper the reliability of the simulations. Here, an integrative assessment of the uncertainty in 21st century glacier runoff is provided based on experiments using the Glacier Evolution Runoff Model (GERM) applied to the catchment of Findelengletscher, Switzerland. GERM is calibrated and validated in a multi-objective approach and is run using nine Regional Climate Models (RCMs) until 2100. Among others, the hydrological impacts of the RCM downscaling procedure, the winter snow accumulation, the surface albedo and the calculation of ice melt and glacier retreat are investigated. All experiments indicate rapid glacier wastage and a transient runoff increase followed by reduced melt season discharge. However, major uncertainties in, e.g., glacier area loss (À100% to À63%) and the change in annual runoff (À57% to +25% relative to today) by 2100 are found. The impact of model assumptions on changes in August runoff is even higher (À94% to À5%). The spread in RCM results accounts for 20-50% of the overall uncertainty in modeled discharge. Initial ice thickness, the amount and spatial distribution of winter snow and the glacier retreat model have the largest effect on the projections, whereas the RCM downscaling procedure, calibration data quality and the melt model (energy balance vs. degree-day approach) are of secondary importance.

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The influence of natural variability and interpolation errors on bias characterization in RCM simulations

Addor

Fischer

2015

JGR Atmospheres

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Climate model simulations are routinely compared to observational data sets for evaluation purposes. The resulting differences can be large and induce artifacts if propagated through impact models. They are usually termed "model biases," suggesting that they exclusively stem from systematic models errors. Here we explore for Switzerland the contribution of two other components of this mismatch, which are usually overlooked: interpolation errors and natural variability. Precipitation and temperature simulations from the RCM COSMO-Community Land Model were compared to two observational data sets, for which estimates of interpolation errors were derived. Natural variability on the multidecadal time scale was estimated using three approaches relying on homogenized time series, multiple runs of the same climate model, and bootstrapping of 30 year meteorological records. We find that although these methods yield different estimates, the contribution of the natural variability to RCM-observation differences in 30 year means is usually small. In contrast, uncertainties in observational data sets induced by interpolation errors can explain a substantial proportion of the mismatch of 30 year means. In those cases, we argue that the model biases can hardly be distinguished from interpolation errors, making the characterization and reduction of model biases particularly delicate. In other regions, RCM biases clearly exceed the estimated contribution of natural variability and interpolation errors, enabling bias characterization and robust model evaluation. Overall, we argue that bias correction of climate simulations needs to account for observational uncertainties and natural variability. We particularly stress the need for reliable error estimates to accompany observational data sets.

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Spectral representation of the annual cycle in the climate change signal

Cited by 103 publications

References 31 publications

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

High uncertainty in 21st century runoff projections from glacierized basins

The influence of natural variability and interpolation errors on bias characterization in RCM simulations

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