Anandhi, A., & Bentley, C. (2018). Predicted 21st century climate variability in southeastern U.S. using downscaled CMIP5 and meta-analysis.170.
Abstract: Trends and variability of the climate in the southeastern United States, including Alabama, Florida, Georgia, Mississippi, North Carolina, South Carolina, and Tennessee was studied for an array of future scenarios in the 21st century. The region is a biodiversity hotspot affected by more billion-dollar disasters than any other region in the country. Assessing the impacts of climate change in southeastern United States is important and often requires knowledge of plausible future climate change (e.g. scenarios of temperature and precipitation change). Although several methods are available in literature to develop plausible scenarios of the changes, there exists a usability gap [gap between what scientists understand as useful information and what users recognize as usable]. A novel conceptual framework that represents the plausible future climate change scenarios in southeastern United States was developed using information from meta-analysis and outputs from similar to 19 Coupled Model Intercomparison Project (CMIP5) Global Climate Models (GCMs) [data analysis] in the form of scenario funnels (represent the plausible trajectories of changes in climate). The systematic literature review provided 33 values of precipitation changes from 15 studies and 35 for temperature changes from 14 studies. In general, the meta-analysis revealed, the precipitation changes observed ranged from -30 to + 35% and temperature changes between - 2 degrees C to 6 degrees C by 2099. Fiftieth percentile of the GCMs predicts no precipitation change and an increase of 2.5 degrees C temperature in the region by 2099. Among the GCMs, 5th and 95th percentile of precipitation changes range between - 40% to 110% and temperature changes between - 2 degrees C to 6 degrees C by 2099. Finally, the usability of scenario information to stakeholders in various southeastern United States ecosystems and guidelines for developing causal chains and feedback loops with three levels of complexity were provided. They include utilizing the information from impact assessment studies, stakeholder's expertise and requirement as well as understanding the potential impacts in ecosystems (e.g. agroecosystems, coastal, wetland) by relating the structural components of an ecosystem, their interactions with each other, within and across ecosystems for improved management and sustainable use of their resources. These would improve understanding of ecosystem functioning for better management and sustainable use of resources. Although the methodology was demonstrated for southeastern United States, it could also be applicable to other regions of the world. However, the scenario funnels, potential impacts on ecosystems and causal chain/loops are subjective to the study region, availability of literature, the changes observed in the literature and data analyzed, the characteristics of the study region, the stakeholder and their requirement.
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Bastola, S. (2013). Hydrologic impacts of future climate change on Southeast US watersheds. Reg. Environ. Change, 13(S1), S131–S139.
Abstract: The hydrological impact of climate change is assessed for 28 watersheds located within the Southeast United States using output from global climate models (GCMs) from the Climate Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) run. Subsequently, the impact of projected change in seasonal streamflow is derived by propagating projected scenarios, generated using changes derived from GCMs and weather generators, through a suite of conceptual hydrological models. Analysis shows that the spread in the magnitude of change in temperature and rainfall for CMIP3 is wider than that for CMIP5. The reduction in the spread among many factors may be attributed to improved physics, model number and resolution, and emission scenarios. The spread in projected change in temperature (precipitation) increases (decreases) from southernmost to northernmost latitude. Hydrological projection with CMIP3 output for the 2070s shows that streamflow decreases for most of the watersheds in spring and summer and increased in fall. In contrast, CMIP5 results show an increase in flow for all seasons except with the high-end scenarios in spring. However, the uncertainty in projections in streamflow is high with model uncertainty dominating emission scenario. The variability in prediction uncertainty among watersheds is partly explained by the variability in wetness index. The probability distribution function for projected seasonal flow for each scenario is markedly wide and therefore reflects that the uncertainty associated with using multiple GCMs from both CMIP3 and CMIP5 experiment is high which makes design and implementation of adaption measure a difficult task.
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Infanti, J. M., Kirtman, B. P., Aumen, N. G., Stamm, J., & Polsky, C. (2020). Aligning Climate Models With Stakeholder Needs: Advances in Communicating Future Rainfall Uncertainties for South Florida Decision Makers. Earth and Space Science, 7(7).
Abstract: Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, "Increasing Confidence in Precipitation Projections for Everglades Restoration," highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as "climate data users") need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate-model simulations based on user-defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a "decision matrix" that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high-resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
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Keellings, D. (2016). Evaluation of downscaled CMIP5 model skill in simulating daily maximum temperature over the southeastern United States. Int. J. Climatol., 36(12), 4172–4180.
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Kozar, M. E., & Misra, V. (2013). Evaluation of twentieth-century Atlantic Warm Pool simulations in historical CMIP5 runs. Clim. Dyn., 41(9-10), 2375–2391.
Abstract: State-of-the-art coupled global climate models are evaluated for their simulation of the Atlantic Warm Pool (AWP). Historical runs from 17 coupled climate models included in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) serve as the basis for this model evaluation study. The model simulations are directly compared to observations and reanalysis data to evaluate the climatological features and variability of the AWP within each individual model. Results reveal that a select number of models�namely the GISS-E2-R, CSIRO-Mk3.6, and MPI-ESM-LR�are successful at resolving an appropriately sized AWP with some reasonable climatological features. However, these three models exhibit an erroneously broad seasonal peak of the AWP, and its variability is significantly underestimated. Furthermore, all of the CMIP5 models exhibit a significant cold bias across the tropical Atlantic basin, which hinders their ability to accurately resolve the AWP.
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Li, J. - L. F., Richardson, M., Hong, Y., Lee, W. - L., Wang, Y. - H., Yu, J. - Y., et al. (2017). Improved simulation of Antarctic sea ice due to the radiative effects of falling snow. Environ. Res. Lett., 12(8), 084010.
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Lintner, B. R., Langenbrunner, B., Neelin, J. D., Anderson, B. T., Niznik, M. J., Li, G., et al. (2016). Characterizing CMIP5 model spread in simulated rainfall in the Pacific Intertropical Convergence and South Pacific Convergence Zones. J. Geophys. Res. Atmos., 121(19), 11,590–11,607.
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Michael, J. - P., Misra, V., & Chassignet, E. P. (2013). The El Nino and Southern Oscillation in the historical centennial integrations of the new generation of climate model. Regional Environmental Change, 13(S1), S121–S130.
Abstract: In this study, we compare the simulation of El Niño and the Southern Oscillation (ENSO) in the historical integrations of 17 Coupled Model Intercomparison Project 5 (CMIP5) models with corresponding observations. The mean state and ENSO variations are analyzed in both the atmosphere and ocean and it is found that most of the CMIP5 models exhibit cold (warm) biases in the equatorial (subtropical eastern) Pacific Ocean sea surface temperature that are reminiscent of the split intertropical convergence zone phenomenon found in previous studies. There is, however, a major improvement in the representation of the power spectrum of the Niño3.4 sea surface temperature variations, which shows that, as in the observations, a majority of the models display a spectral peak in the 2�7 year range, have a near-linear relationship with the displacement of the equatorial thermocline and exhibit a robust atmospheric response to ENSO variations. Several issues remain such as erroneous amplitudes in the Niño3.4 sea surface temperature spectrum�s peak and a width of the spectral peak that is either too broad or too narrow. It is also seen that most CMIP5 models unlike the observations extend the ENSO variations in the equatorial Pacific too far westward beyond the dateline and there is very little asymmetry in event duration between the warm and cold phases. ENSO variability forces a dominant mode of rainfall variability in the southeastern United States, especially in the boreal winter season. The CMIP5 exhibited a wide range of response in this metric with several displaying weak to nonexistent, some showing relatively strong, and one indicating excessively zonally symmetric teleconnection over the southeastern United States.
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Sharma, A., & Anandhi, A. (2021). Temperature based indicators to develop adaptive responses for crop production in Florida, USA. Ecological Indicators, 121.
Abstract: Crop failure temperatures (CFTs) are critical upper threshold temperatures above which plant growth and development stop. Climate variability with CFTs has an essential impact on agriculture, which leads to a decrease in plant yield to nearly zero. This study innovatively combines data analysis and analysis of published literature to develop causal chains/loops using Driver-Pressure-State-Impact-Responses (DPSIR) framework. In data analysis, CFTs trends were estimated from 21 models participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the historical (1950–2005) and future scenarios (RCP 8.5, 2006–2100) at a spatial resolution of 0.125°x0.125° over Florida region. From the scenario funnel plots, it is evident that the frequency of number days above CFTs was found to be increasing at the rate of 2 days/year, and maximum mean temperature intensity was found in the range of 0.02 to 0.04 °C/year till 21st century. The causal chain and loop help to understand the complex structure and feedback mechanism for CFTs. This also helps in bridging the gap between climate and crop to address the adaptation strategies if the impacts are known. Adaptation strategies from the effects of the crops found to be promising to mitigate the effects of climate on crop and which can be used by the stakeholders and managers for their own use.
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