A strategy to effectively make use of large volumes of climate data for climate change adaptation

Benestad, Rasmus E.; Parding, Kajsa M.; Dobler, Andreas; Mezghani, Abdelkader

doi:10.1016/j.cliser.2017.06.013

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…It is important to note that regional climate models were used to downscale a small number (four for RCP4.5) of GCMs, and that even one single GCM can produce very different local and regional projections due to the presence of pronounced nondeterministic natural variability on decadal timescales (Deser et al 2012). Hence, estimates based on a small set of climate model simulations are subject to the "law of small numbers" (Benestad et al 2017), for which the estimates may be misleading, the true range underestimated and the real uncertainties appear to be smaller than they really are. Mezghani et al (2018) tested the influence of selecting a subset of global climate models to drive two downscaling strategies (Empirical-Statistical and Dynamical Downscaling, i.e., ESD and DD, respectively) on the resultant regional and local climate information.…”

Section: Climate Projectionsmentioning

confidence: 99%

Assessment of climate change and associated impact on selected sectors in Poland

et al. 2018

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The present paper offers a brief assessment of climate change and associated impact in Poland, based on selected results of the Polish-Norwegian CHASE-PL project. Impacts are examined in selected sectors, such as water resources, natural hazard risk reduction, environment, agriculture and health. Results of change detection in long time series of observed climate and climate impact variables in Poland are presented. Also, projections of climate variability and change are provided for time horizons of 2021-2050 and 2071-2100 for two emission scenarios, RCP4.5 and RCP8.5 in comparison with control period, 1971-2000. Based on climate projections, examination of future impacts on sectors is also carried out. Selected uncertainty issues relevant to observations, understanding and projections are tackled as well.

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Section: Climate Projectionsmentioning

confidence: 99%

Assessment of climate change and associated impact on selected sectors in Poland

et al. 2018

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“…Probabilities account for such variability, and the analysis presented here made use of the median of the simulated temperature from large multi-model ensembles and a Bayesian-inspired approach to account for both natural variability and model differences. Such ensembles cannot be considered to be unbiased statistical samples (Benestad et al, 2017b) as different models have similar biases since they share many components. The model differences, however, have been found to be less pronounced than the year-to-year variations and can for all intents and purposes be used as an imperfect description of the statistical spread when better information is lacking.…”

Section: Discussionmentioning

confidence: 99%

“…People have learnt to cope with climate variations and severe weather over historical times and have adapted to various weather-related risks. In this respect, climate can be regarded as the statistical description of various weather variables (Benestad et al, 2017a), giving a picture of "typical" types of weather and what to expect. This statistical description includes the mean, variance, autocorrelation, periodicity, and duration of various climatological events.…”

Section: Weather Statistics and Societymentioning

confidence: 99%

“…We used ESD to make future projections for the February-April mean daily maximum temperature for a set of locations in India (see the Supplement for map) with multimodel ensembles as in : 108 runs of the intermediate-emission scenario Representative Concentration Pathway (RCP)4.5, 81 runs of the high-emission scenario RCP8.5, and 65 runs of the low-emission scenario RCP2.6. Using large multi-model ensembles gave more robust results and alleviated limitations caused by small sample sizes and "the law of small numbers" (Kahneman, 2012) due to larger sampling fluctuations with smaller samples (Benestad et al, 2017b). A principal component analysis (PCA) was used to represent the local temperature (predictands) in order to enhance the signal-to-noise ratio (Benestad et al, 2015a), and the ESD model involved a stepwise multiple linear regression where the predictand was represented by PCAs describing the February-April mean maximum temperature T max .…”

Section: Temperature Projectionsmentioning

confidence: 99%

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Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

Benestad

Oort

Justino³

et al. 2018

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

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“…Having performed a subjective evaluation of the results obtained by the CCI method, it is clear that the automatically identified Mediterranean cyclones and, subsequently, the trajectory followed by each of them can also be found in the daily reanalysis maps that show the main fields at standard atmospheric levels. This shows and proves that the present results are correct and the method used herein is reliable as already stated when used in important and complex studies (Benestad and Chen, ; Neu, ; Benestad et al ., , ).…”

Section: Resultsmentioning

confidence: 99%

Objective identification of Mediterranean cyclones and their trajectories towards Romania

Catrina

Ștefan

Craciun

2019

Meteorological Applications

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A mathematical algorithm for the automatic detection and tracking of Mediterranean cyclones is presented. The objective of this study is identifying those Mediterranean cyclones that are engaged on various trajectories towards the countries bordering the Black Sea. Mediterranean cyclones with trajectories towards Romania have been identified from a six‐hourly reanalysis of mean sea level pressure (MSLP) data set for a period of 37 years (January 1980–December 2016) within the chosen domain 15 ° W–40 ° E/30 ° N–50 ° N. The cyclone identification was performed using a method known as calculus cyclone identification (CCI), which was adapted for the Mediterranean Sea region. The results obtained using the objective method were compared with the Mediterranean cyclone trajectory data obtained about 55 years ago by Romanian researchers who made use of subjective methods. A summary of the climatology of Mediterranean cyclones affecting Romania was also performed. To demonstrate the accuracy of the objective method adapted for the cyclones engaged on trajectories towards Romania, a subjective analysis using the potential vorticity and geopotential height fields was made. The results confirmed that the method can be successfully implemented by the Romanian Meteorological Service for the detection of Mediterranean cyclones. This method will, therefore, be a useful tool for meteorologists in their forecasting activity.

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A strategy to effectively make use of large volumes of climate data for climate change adaptation

Cited by 19 publications

References 34 publications

Assessment of climate change and associated impact on selected sectors in Poland

Assessment of climate change and associated impact on selected sectors in Poland

Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

Objective identification of Mediterranean cyclones and their trajectories towards Romania

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