Assessment of natural climate variability using a weather generator

Brisson, Erwan; Demuzere, Matthias; Willems, Patrick; Lipzig, Nicole Van

doi:10.1007/s00382-014-2122-8

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…Empirical mode decomposition (EMD) [Huang et al, 1998] is an algorithm to iteratively extract the intrinsic time scale components from any given series. It has already been applied in studies of climate variability [Brisson et al, 2015;Coughlin and Tung, 2005;Molla et al, 2011;Pegram et al, 2008] and climate effects on plant phenology [Guan, 2014].…”

Section: Interannual Extraction Tools 411 Ensemble Empirical Mode mentioning

confidence: 99%

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

et al. 2016

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Ecosystem sensitivity to climate variability varies across East Africa, and identifying the determinant factors of this sensitivity is crucial to assessing region‐wide vulnerability to climate change and variability. Such assessment critically relies on spatiotemporal data sets with inherent uncertainty, on new processing techniques to extract interannual variability at a priori unknown time scales and on adequate statistical models to test for biogeographical effects on vegetation‐precipitation relationships. In this study, interannual variability in long‐term records of normalized difference vegetation index and satellite‐based precipitation estimates was detected using ensemble empirical mode decomposition and standardized precipitation index with varying accumulation periods. Environmental effect modeling using additive models with spatially correlated effects showed that ecosystem sensitivity is primarily predicted by biogeographical factors such as annual precipitation distribution (reaching maximum sensitivity at 500 mm yr−1), vegetation type and structure, ocean‐climate coupling, and elevation. The threat of increasing climate variability and extremes impacting productivity and stability of ecosystems is most imminent in semiarid grassland and mixed cropland ecosystems. The influence of oceanic phenomena such as El Niño–Southern Oscillation and Indian Ocean Dipole is foremost reflected in precipitation variability, but prolonged episodes also pose risks for long‐term degradation of tree‐rich ecosystems in the East African Great Lakes region.

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Section: Interannual Extraction Tools 411 Ensemble Empirical Mode mentioning

confidence: 99%

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

et al. 2016

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“…Huang et al (1999) demonstrated that EMD is generally successful in retrieving the physically meaningful signals hidden in time series. In some Brisson et al (2015). The significance is defined by the original trend having variability higher than the variability of a trend derived from a randomized time series.…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Multidecadal change in streamflow associated with anthropogenic disturbances in the tropical Andes

Molina

Vanacker

Brisson

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Andean headwater catchments are an important source of freshwater for downstream water users. However, few long-term studies exist on the relative importance of climate change and direct anthropogenic perturbations on flow regimes in these catchments. In this paper, we assess change in streamflow based on long time series of hydrometeorological data and land cover reconstructions in the Pangor catchment (282 km 2 ) located in the tropical Andes. Three main land cover change trajectories can be distinguished during the period 1963-2009: (1) expansion of agricultural land by an area equal to 14% of the catchment area (or 39 km 2 ) in 46 years' time, (2) deforestation of native forests by 11 % (or −31 km 2 ) corresponding to a mean rate of 67 ha yr −1 , and (3) afforestation with exotic species in recent years by about 5 % (or 15 km 2 ). Over the time period 1963-2009, about 50 % of the 64 km 2 of native forests was cleared and converted to agricultural land. Given the strong temporal variability of precipitation and streamflow data related to El Niño-Southern Oscillation, we use empirical mode decomposition techniques to detrend the time series. The long-term increasing trend in rainfall is remarkably different from the observed changes in streamflow, which exhibit a decreasing trend. Hence, observed changes in streamflow are not the result of long-term change in precipitation but very likely result from anthropogenic disturbances associated with land cover change.

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“…Observational data of the earth's climate usually cover only several decades, and are thus not long enough to capture this mid-to long-term variability (Hawkins et al 2016). Brisson et al (2015) for example, used a weather generator to quantify uncertainties in estimating climatic conditions from a 30-year period as recommended by the World Meteorological Organization (WMO), and showed that these uncertainties can well exceed 15% for extreme precipitation. This lack of data makes it very difficult or nearly impossible to use observations for the quantification of natural variability on longer time scales.…”

Section: Introduction: Uncertainties In Climate Projectionsmentioning

confidence: 99%

Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble

2019

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Uncertainties in climate model ensembles are still relatively large. Besides scenario and model response uncertainty, natural variability is another important source of uncertainty. To study regional natural variability on timescales of several decades and more, observations are often too sparse and short. Regional Climate Models (RCMs) can be used to overcome this lack of useful data at high spatial resolutions. In this study, we compare a new 50-member single RCM large ensemble (CRCM5-LE) with an ensemble of 22 EURO-CORDEX models for seasonal temperature and precipitation at 0.11° grid size over Europe-all driven by the RCP 8.5 scenario. This setup allows us to quantify the contribution of natural/modelinternal variability on the total uncertainty of a multi-model ensemble. The variability of climate change signals within the two ensembles is compared for three future periods (2020-2049, 2040-069 and 2070-2099). Results show that the single model spread is usually smaller than the multi-model spread for temperature. Similar variabilities can mostly be found in the near future (and to a lesser extent in the mid future) during winter and spring, especially for northern and central parts of Europe. The contribution of internal variability is generally higher for precipitation. In the near future almost all seasons and regions show similar variabilities. In the mid and far future only fall, summer and spring still show similar variabilites. There is a significant decrease of the contribution of internal variability over time for both variables. However, even in the far future for most regions and seasons 25-75% of the overall variability can be explained by internal variability.

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Assessment of natural climate variability using a weather generator

Cited by 14 publications

References 24 publications

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

Multidecadal change in streamflow associated with anthropogenic disturbances in the tropical Andes

Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble

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