Bias correction of global circulation model outputs using artificial neural networks

Climate studies and effective environmental management plans require unbiased climate datasets. This study develops a new bias correction approach using a three layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, net longwave and shortwave radiation are used as inputs for the bias correction of temperature. Precipitation at lag zero, one, two, and three, and the standard deviation from 3 by 3 neighbors around the pixel of interest are the inputs into the ANN bias correction of precipitation. The data are provided by the Community Climate System Model (CCSM3). Results show that the trained ANN can markedly reduce the estimation error and improve the correlation and probabilistic structure of the bias-corrected variables for calibration and validation periods. The ANN outperforms linear regression (LR), which is used for comparison purposes. The ability of the regression models (linear and ANN) to regionalize the study domain is investigated by defining the minimum number of training pixels necessary to achieve a good level of bias correction performance over the entire domain. Results confirm that it is possible to identify regions in terms of physical features such as land cover, topography, and climatology over which the trained models at a few pixels can do well. The new approach saves computational demands, time, and memory usage and it can be used for other climate models efficiently.Ph.D

Characterization of aerosol types over Lake Urmia Basin

Atmospheric aerosols affect the Earth's climate, air quality, and thus human health. This study used the Aerosol Optical Depth (AOD) and the Ångström exponent to cluster different particle types over the Lake Urmia Basin. This classification found desert dust and marine (mixed with continental or local-pollution aerosols) as two main aerosol types over the region, while their sources are not well defined. Although different air masses and wind circulation over the study domain in varied months can help to distinguish aerosol sources, measurements are crucial for a complete evaluation

A probabilistic climate change assessment for Europe

Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near-future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis of four subregions of Europe, namely east (G1), north (G2), west/south (G3), and UK/Ireland (G4), shows that G1 and G4 are expected to have the largest increase in temperature and precipitation extremes, respectively. Although maximum increases in temperature in G3 and G4 occur at larger percentiles, G1 and G2 experience maximum increases at both large and small percentiles. The maximum increase of precipitation over the study domain, however, occurs mainly at larger extremes. To quantify changes in the distribution of projection (2020–2049) relative to the historical reference (1976–2005), two measures are defined, namely a change in occurrences (KS statistic) and intensities at different quantiles (Δ). Results confirm that the temperature distribution tends to shift to higher temperatures. Changes in distribution and extremes of precipitation are spatially variable. Furthermore, extremes are expected to be intensified under RCP 8.5. The quantile analysis and defined measures reveal changes in the entire probability distribution, reflecting possible climate changes in the future.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water ResourcesWater Managemen

A Bias-corrected Data set of Climate Model Outputs at Uniform Space-Time Resolution for Land Surface Modeling over Amazonia

Developing high-quality long-term data sets at uniform space–time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model (CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model (RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change (IPCC\u27s) historical (20C3M) and future (A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set (PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin