Simple Nonparametric Techniques for Exploring Changing Probability Distributions of Weather

2005 ◽  
Vol 18 (21) ◽  
pp. 4344-4354 ◽  
Author(s):  
Christopher A. T. Ferro ◽  
Abdelwaheb Hannachi ◽  
David B. Stephenson
Keyword(s):  
Probability Distribution ◽  
Minimum Temperature ◽  
Climate Model ◽  
Probability Distributions ◽  
Regional Climate ◽  
Distribution Functions ◽  
Daily Minimum Temperature ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
Projected Changes

Abstract Anthropogenic influences are expected to cause the probability distribution of weather variables to change in nontrivial ways. This study presents simple nonparametric methods for exploring and comparing differences in pairs of probability distribution functions. The methods are based on quantiles and allow changes in all parts of the probability distribution to be investigated, including the extreme tails. Adjusted quantiles are used to investigate whether changes are simply due to shifts in location (e.g., mean) and/or scale (e.g., variance). Sampling uncertainty in the quantile differences is assessed using simultaneous confidence intervals calculated using a bootstrap resampling method that takes account of serial (intraseasonal) dependency. The methods are simple enough to be used on large gridded datasets. They are demonstrated here by exploring the changes between European regional climate model simulations of daily minimum temperature and precipitation totals for winters in 1961–90 and 2071–2100. Projected changes in daily precipitation are generally found to be well described by simple increases in scale, whereas minimum temperature exhibits changes in both location and scale.

scholarly journals Development of probability distributions for regional climate change from uncertain global mean warming and an uncertain scaling relationship

2007 ◽  
Vol 11 (3) ◽  
pp. 1097-1114 ◽  
Author(s):  
B. Hingray ◽  
A. Mezghani ◽  
T. A. Buishand
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Probability Distributions ◽  
Regional Climate ◽  
Natural Variability ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
Global Mean

Abstract. To produce probability distributions for regional climate change in surface temperature and precipitation, a probability distribution for global mean temperature increase has been combined with the probability distributions for the appropriate scaling variables, i.e. the changes in regional temperature/precipitation per degree global mean warming. Each scaling variable is assumed to be normally distributed. The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted within the framework of the EU-funded project PRUDENCE, using an Analysis Of Variance (ANOVA). For the area covered in the 2001–2004 EU-funded project SWURVE, five case study regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). The resulting regional climate changes for 2070–2099 vary quite significantly between CSRs, between seasons and between meteorological variables. For all CSRs, the expected warming in summer is higher than that expected for the other seasons. This summer warming is accompanied by a large decrease in precipitation. The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer because of the differences between regional climate models. Differences between the spatial climate-change patterns of global climate model simulations make significant contributions to the uncertainty of the scaling ratio for temperature. However, no meaningful contribution could be found for the scaling ratio for precipitation due to the small number of global climate models in the PRUDENCE project and natural variability, which is often the largest source of uncertainty. In contrast, for temperature, the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. For the regional change in precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.

scholarly journals Projected Changes in Extreme Temperature and Precipitation Indices Over CORDEX-MENA Domain

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 622
Author(s):  
Tugba Ozturk ◽  
F. Sibel Saygili-Araci ◽  
M. Levent Kurnaz
Keyword(s):  
Regional Climate Model ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Extreme ◽  
Pronounced Increase ◽  
Temperature And Precipitation ◽  
Projected Changes ◽  
Extreme Indices

In this study, projected changes in climate extreme indices defined by the Expert Team on Climate Change Detection and Indices were investigated over Middle East and North Africa. Changes in the daily maximum and minimum temperature- and precipitation- based extreme indices were analyzed for the end of the 21st century compared to the reference period 1971–2000 using regional climate model simulations. Regional climate model, RegCM4.4 was used to downscale two different global climate model outputs to 50 km resolution under RCP4.5 and RCP8.5 scenarios. Results generally indicate an intensification of temperature- and precipitation- based extreme indices with increasing radiative forcing. In particular, an increase in annual minimum of daily minimum temperatures is more pronounced over the northern part of Mediterranean Basin and tropics. High increase in warm nights and warm spell duration all over the region with a pronounced increase in tropics are projected for the period of 2071–2100 together with decrease or no change in cold extremes. According to the results, a decrease in total wet-day precipitation and increase in dry spells are expected for the end of the century.

scholarly journals An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets

2020 ◽  
Vol 12 (4) ◽  
pp. 2959-2970
Author(s):  
Maialen Iturbide ◽  
José M. Gutiérrez ◽  
Lincoln M. Alves ◽  
Joaquín Bedia ◽  
Ruth Cerezo-Mota ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Atmospheric Model ◽  
Future Climate Change ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation ◽  
Scatter Plots ◽  
Projected Changes

Abstract. Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

Quantifying uncertainty in projections of future European climate: a multi-model multi-method approach

2020 ◽  
Author(s):  
Lukas Brunner ◽  
Carol McSweeney ◽  
Daniel Befort ◽  
Chris O'Reilly ◽  
Ben Booth ◽  
...  
Keyword(s):  
Climate Model ◽  
Probability Distributions ◽  
Internal Variability ◽  
Precipitation Change ◽  
Future Climate Change ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
European Climate ◽  
Climate Model Simulations ◽  
Quantifying Uncertainty

<p>Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. Different approaches to constrain, filter, or weight climate model simulations into probabilistic projections have been proposed to provide such estimates. Here six methods are applied to European climate projections using a consistent framework in order to allow a quantitative comparison.  Focus is given to summer temperature and precipitation change in three different spatial regimes in Europe in the period 2041-2060 relative to 1995-2014. The analysis draws on projections from several large initial condition ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5.  <br>The methods included are diverse in their approach to quantifying uncertainty, and include those which apply weighting schemes based on baseline performance and inter-model relationships, so-called ASK (Allen, Stott and Kettleborough) techniques which use optimal fingerprinting to scale the scale the response to external forcings, to those found in observations and Bayesian approaches to estimating probability distributions. Some of the key differences between methods are the uncertainties covered, the treatment of internal variability, and variables and regions used to inform the methods. In spite of these considerable methodological differences, the median projection from the multi-model methods agree on a statistically significant increase in temperature by mid-century by about 2.5°C in the European average. The estimates of spread, in contrast, differ substantially between methods. Part of this large difference in the spread reflects the fact that different methods attempt to capture different sources of uncertainty, and some are more comprehensive in this respect than others. This study, therefore, highlights the importance of providing clear context about how different methods affect the distribution of projections, particularly the in the upper and lower percentiles that are of interest to 'risk averse' stakeholders. Methods find less agreement in precipitation change with most methods indicating a slight increase in northern Europe and a drying in the central and Mediterranean regions, but with considerably different amplitudes. Further work is needed to understand how the underlying differences between methods lead to such diverse results for precipitation. </p>

scholarly journals An update of IPCC climate reference regions for subcontinental analysis of climate model data: Definition and aggregated datasets

2020 ◽  
Author(s):  
Maialen Iturbide ◽  
José Manuel Gutiérrez ◽  
Lincoln Muniz Alves ◽  
Joaquín Bedia ◽  
Ezequiel Cimadevilla ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate Change ◽  
Temperature And Precipitation ◽  
Data Definition ◽  
Definition Of ◽  
Projected Changes

Abstract. Several sets of reference regions have been proposed in the literature for the regional synthesis of observed and model-projected climate change information. A popular example is the set of reference regions introduced in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX) based on a prior coarser selection and then slightly modified for the 5th Assessment Report of the IPCC. This set was developed for reporting sub-continental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes (the typical resolution of the 5th Climate Model Intercomparison Projection, CMIP5, climate models was around 2º). These regions have been used as the basis for several popular spatially aggregated datasets, such as the seasonal mean temperature and precipitation in IPCC regions for CMIP5. Here we present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher model resolution (around 1º for CMIP6). As a result, the number of regions increased to 43 land plus 12 open ocean, better representing consistent regional climate features. The paper describes the rationale followed for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and shapefile together with companion R and Python notebooks to illustrate their use in practical problems (trimming data, etc.). We also describe the generation of a new dataset with monthly temperature and precipitation spatially aggregated in the new regions, currently for CMIP5 (for backwards consistency) and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter diagrams to offer guidance on the likely range of future climate change at the scale of reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository; https://github.com/SantanderMetGroup/ATLAS, doi:10.5281/zenodo.3688072 (Iturbide et al., 2020).

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