Tag: Climate Change Downscaling Techniques

Abstract

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the \(\Delta\) change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than \(1\,^\circ\) C for temperatures) these differences are generally smaller than the models’ inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the \(\Delta\) change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the \(\Delta\) changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles.

Abstract

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.

Abstract

Background

Cross-landscape fuel moisture content is highly variable but not considered in existing fire danger assessments. Capturing fuel moisture complexity and its associated controls is critical for understanding wildfire behavior and danger in emerging fire-prone environments that are influenced by local heterogeneity. This is particularly true for temperate heathland and peatland landscapes that exhibit spatial differences in the vulnerability of their globally important carbon stores to wildfire. Here we quantified the range of variability in the live and dead fuel moisture of Calluna vulgaris across a temperate fire-prone landscape through an intensive fuel moisture sampling campaign conducted in the North Yorkshire Moors, UK. We also evaluated the landscape (soil texture, canopy age, aspect, and slope) and micrometeorological (temperature, relative humidity, vapor pressure deficit, and windspeed) drivers of landscape fuel moisture variability for temperate heathlands and peatlands for the first time.

Results

We observed high cross-landscape fuel moisture variation, which created a spatial discontinuity in the availability of live fuels for wildfire spread (fuel moisture < 65%) and vulnerability of the organic layer to smoldering combustion (fuel moisture < 250%). This heterogeneity was most important in spring, which is also the peak wildfire season in these temperate ecosystems. Landscape and micrometeorological factors explained up to 72% of spatial fuel moisture variation and were season- and fuel-layer-dependent. Landscape factors predominantly controlled spatial fuel moisture content beyond modifying local micrometeorology. Accounting for direct landscape–fuel moisture relationships could improve fuel moisture estimates, as existing estimates derived solely from micrometeorological observations will exclude the underlying influence of landscape characteristics. We hypothesize that differences in soil texture, canopy age, and aspect play important roles across the fuel layers examined, with the main differences in processes arising between live, dead, and surface/ground fuels. We also highlight the critical role of fuel phenology in assessing landscape fuel moisture variations in temperate environments.

Conclusions

Understanding the mechanisms driving fuel moisture variability opens opportunities to develop locally robust fuel models for input into wildfire danger rating systems, adding versatility to wildfire danger assessments as a management tool.

Abstract

Large-scale temperature projections need to be downscaled to river basin scale to facilitate a regional scale climate change impact assessment. A multi-stage statistical downscaling procedure is proposed in the current study, the first stage captures the climate change signals from the simulations of general circulation models (GCMs) by spatially downscaling the monthly GCM simulations. The second stage disaggregates the spatially downscaled monthly series to a daily scale by a weather generator which adds the regional climatic information into the spatially downscaled time series. A distribution-free post-processing shuffling is finally performed to rebuild the intervariable correlation of downscaled temperatures with regional rainfall which is important in reliable projection of streamflow. The procedure is validated by downscaling the maximum and minimum temperatures over the Bharathapuzha catchment in India for the period 1951–2005. The downscaled series of temperature shows Normalised Root Mean Square Error (NRMSE) less than 0.09 and correlation coefficients greater than 0.4. The ability of the procedure in capturing non-stationarity in the climate is also analysed by its performance in different phases of ENSO.

Abstract

Three cumulus and five microphysics parameterization schemes of the Weather Research and Forecasting model (WRF) are the basis for simulating ten specific meteorological events of the Paraíba do Sul River Basin (PSRB) in Southeast Brazil. The cases studied are frontal wave systems, thermodynamic instability, and the South Atlantic Convergence Zone (SACZ). Each parameterization combination generated 15 simulations for each event, resulting in 150 tests. The primary domain has a horizontal resolution of 8.0 km and the nested 2.6 km resolution. Three analysis tools underlie the study: (i) punctual verification of the first 24 h of precipitation forecast, using the Taylor diagram; (ii) verification of the prediction of precipitation using categorical binary variable and (iii) the Model´s ability to reproduce patterns of the spatial distribution of precipitation. The Taylor diagram suggests that the combination of the Morrison Double moment and Multiscale Kain–Fritsch schemes produce the best results. The categorical verification indicates that, for dynamic/convective events, the Morrison Double moment and Multiscale Kain–Fritsch and WRF Double Moment 6–class sets showed the best indices. Some configurations presented reliable results for exclusively convective events, and WRF Single–moment 6–class and Grell–Freitas Ensemble is the best combination. The Morrison Double moment and Multiscale Kain–Fritsch parameterizations yielded the best performance for the spatial distribution. Overall, the schemes tested perform better for the upstream region, i.e., the area of greater water uptake for the basin.

Abstract

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ET_o) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ET_o PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series.

Abstract

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The presented data comprise the fusion onto the American Community Survey (ACS) microdata of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Travel Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015–2019. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The underlying statistical techniques, designed under the fusionACS project, are included in an open-source R package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.

Abstract

This study aims at estimating the inherent variability of microscale boundary-layer flows and its impact on air pollutant dispersion in urban environments. For this purpose, we present a methodology combining high-fidelity large-eddy simulation (LES) and a stationary bootstrap algorithm, to estimate the internal variability of time-averaged quantities over a given analysis period thanks to sub-average samples. A detailed validation of an LES microscale air pollutant dispersion model in the framework of the Mock Urban Setting Test (MUST) field-scale experiment is performed. We show that the LES results are in overall good agreement with the experimental measurements of wind velocity and tracer concentration, especially in terms of fluctuations and peaks of concentrations. We also show that both LES estimates and the MUST experimental measurements are subject to significant internal variability, which is therefore essential to take into account in the model validation. Moreover, we demonstrate that the LES model can accurately reproduce the observed internal variability.

Abstract

As a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production.

Abstract

The tails of the probability distribution host extremes. The distributions are typically classified into heavy or light-tailed distributions subjected to their tail behavior, out of which the former signifies frequent happenings of extreme events. The present study demonstrates the analysis where the outputs from 13 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used to evaluate changes in the tail behavior of precipitation extremes that will preside over India for the twenty-first century. A straightforward empirical index known as the “obesity index” (OB) is utilized to measure the tail heaviness for each of the 4801 daily precipitation records over India for historical (1970–2019) and future (2020–2100) time periods. The same approach was used to characterize daily precipitation tails in the Indian meteorological subdivisions and across different climate types during various periods. The results highlight that heavy-tailed distributions are well-suited for daily precipitation extremes in India, with OB values above 0.75 observed in nearly all grids for both present and future scenarios. Notably, in the case of the shared socioeconomic pathway (SSP) 585 climate scenario, which is the worst climate scenario, approximately 42.82% of grids exhibit the highest range of OB from 0.85 to 0.9 relative to other SSP scenarios. The findings also show that the largest to smallest heavy tails are associated with major climate types E (polar), B (arid), A (tropical), and C (temperate). Large heavy-tailed extremes are observed in ET, BSh, BWh, and Aw for climate subtypes, while relatively lighter-tailed extremes were observed in Am and Cwb. Furthermore, the variation in the OB is found to be non-linear with the elevation. In climatic zones Aw, BSh, Cwa, and ET, a U-shaped pattern is observed, while in climate zone Cwb, it shows a concave increase. Conversely, curves are convex decreasing for As, BWh, Csa, and convex increasing for zone Am. The conclusions from this study can help policymakers in designing adaptation plans in response to the anticipated effects of climate change.