Ali Ahmadalipour

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

Remote Sensing of Drought: Vegetation, Soil Moisture, and Data Assimilation

Application of remote sensing is emerging for operational drought monitoring and early warning as it offers opportunities for assessing drought from different perspectives. This chapter provides an overview of the advances in monitoring different types of drought using satellite remote-sensing observations with an example on agricultural drought assessment over the continental U.S. While the main constraint in remote sensing of drought is attributed to limited duration of records, this can be overcome by merging the remote-sensing observations with model simulations through data assimilation. The application of data assimilation as a promising approach is described for drought monitoring over the Pacific Northwest US.

Escalating heat-stress mortality risk due to global warming in the Middle East and North Africa (MENA)

Climate change will substantially exacerbate extreme temperature and heatwaves. The impacts will be more intense across the Middle East and North Africa (MENA), a region mostly characterized by hot and arid climate, already intolerable for human beings in many parts. In this study, daily climate data from 17 fine-resolution Regional Climate Models (RCMs) are acquired to calculate wet-bulb temperature and investigate the mortality risk for people aged over 65 years caused by excessive heat stress across the MENA region. Spatially adaptive temperature thresholds are implemented for quantifying the mortality risk, and the analysis is conducted for the historical period of 1951-2005 and two future scenarios of RCP4.5 and RCP8.5 during the 2006-2100 period. Results show that the mortality risk will increase in distant future to 8-20 times higher than that of the historical period if no climate change mitigation is implemented. The coastal regions of the Red sea, Persian Gulf, and Mediterranean Sea indicate substantial increase in mortality risk. Nonetheless, the risk ratio will be limited to 3-7 times if global warming is limited to 2 °C. Climate change planning and adaptation is imperative for mitigating heat-related mortality risk across the region.

Multi-dimensional assessment of drought vulnerability in Africa: 1960–2100

Drought vulnerability is a complex concept that identifies the capacity to cope with drought, and reveals the susceptibility of a system to the adverse impacts of drought. In this study, a multi-dimensional modeling framework is carried out to investigate drought vulnerability at a national level across the African continent. Data from 28 factors in six different components (i.e. economy, energy and infrastructure, health, land use, society, and water resources) are collected for 46 African countries during 1960-2015, and a composite Drought Vulnerability Index (DVI) is calculated for each country. Various analyses are conducted to assess the reliability and accuracy of the proposed DVI, and the index is evaluated against historical observed drought impacts. Then, regression models are fitted to the historical time-series of DVI for each country, and the models are extrapolated for the period of 2020-2100 to provide three future scenarios of DVI projection (low, medium, and high) based on historical variations and trends. Results show that Egypt, Tunisia, and Algeria are the least drought vulnerable countries, and Chad, Niger, and Malawi are the most drought vulnerable countries in Africa. Future DVI projections indicate that the difference between low- and high-vulnerable countries will increase in future, with most of the southern and northern African countries becoming less vulnerable to drought, whereas the majority of central African countries indicate increasing drought vulnerability. The projected DVIs can be utilized for long-term drought risk analysis as well as strategic adaptation planning purposes.