Multi-scale climate change modeling study over the Greater Horn of Africa

climate change;regional climate modeling;empirical analysis;Greater Horn of Africa

Bowden, Jared Heath ; Lian Xie, Committee Member,Sankarasubramanian Arumugam, Committee Member,Fredrick Semazzi, Committee Chair,Anantha Aiyyer, Committee Member,Bowden, Jared Heath ; Lian Xie ; Committee Member ; Sankarasubramanian Arumugam ; Committee Member ; Fredrick Semazzi ; Committee Chair ; Anantha Aiyyer ; Committee Member

University:North Carolina State University

关键词: climate change;    regional climate modeling;    empirical analysis;    Greater Horn of Africa;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4392/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

There has been limited regional climate modeling (RCM) studies of climate change over theGreater Horn of Africa because of challenges of modeling tropical precipitation with a limitedobservational rainfall network. This study customized a RCM model with particular interestin precipitation process using several precipitation data sets for validation. Various convective schemes and micro-physics sensitivities were performed. It was found that the convectivescheme of MIT-Emanuel in conjunction with reducing the relative humidity threshold for cloudformation provided the most realistic simulation in terms of spatial distribution, convectivepartition, rainfall totals and temperature bias when compared with observations.The above RCM customization was run for approximately 40 years to determine the modelsability to capture inter-annual variability and the possible climate change fingerprint over theregion. The RCM is able to capture the inter-annual variability for all places and seasons for temperature. However, the positive precipitation bias limits the models ability to capture inter-annual variability of precipitation. Despite, the low inter-annual precipitation correlation, the RCM is able to simulate large scale changes in the rainfall pattern associated with the possible climate change fingerprint and the annual precipitation cycle associated with the monsoon.Since the model was able to capture possible changes associated with climate change, themodel was downscaled for climate change simulations.The Finite Volume GCM (FVGCM) is used as the lateral boundary forcing for A2 scenarioRCM climate change simulations. The FVGCM was compared with the other IPCC modelsand found to perform within the range during the contemporary climate for circulation, precipitation and temperature. Our analysis concluded that the FVGCM has a cool and wet biascompared to the other GCMs. The RCM future climate simulations, using an A2 emission scenario, show that average temperature patterns in arid to semi-arid regions are likely to have thelargest temperature increases when coupled to increased drying. Coastal locations are likely toexperience the smallest temperature increase in response to increased likelihood of enhancedprecipitation east of the Great Lakes and the response to the ocean’s thermal inertia. Daily temperature mean increase of 2.5C is found with a shift toward more extreme heat waves. Thereis also a clear shift for more intense precipitation for the eastern GHA with some localized regions having a shift in the rainfall frequency.We caution the interpreation of the dynamical downscale results because we have onlydownscaled one GCM and biases in the GCM lateral boundary forcing and internal errors inthe RCM itself. The approach was limited to one GCM because of the computational expenseof dynamically downscaling and locating 6 hourly ICBC for both the contemporary and futureclimate. We test a “climatologicalâ€ICBC approach for climate change simulations to limit thecost factor of the simulations. We find the approach is likely to be of benefit in simulatingthe spatial distributions for the GHA region when the boundary is far removed from regionof interest. This method may be applied to other regions within the tropics and likely usefuldynamical downscale physics ensembles and mutliple GCMs. Based on the application of thedynamical downscale results, we have provided a framework to help provide a clear approachfor future dynamical downscale climate change simulations.