To date, there are a high volume of studies concerning climate change impact assessments in ecosystems. Meta‐analysis, scenario development, and causal chains/loops have been used as tools in these assessments as well as in decision making either individually or combined in pairs. There exists a need to develop decision support tools that improve the linkage between climate‐impacts research and planning, management, adaptation, and mitigation decisions by providing quantitative and timely information to stakeholders and managers. The overall goal is to address this need. A specific objective was to develop a decision support tool in eco‐hydrological applications that combine three components: meta‐analysis, scenario development, and causal chains/loop. The developed tool is novel, warranted, and timely. The use of the tool is demonstrated for Florida. The meta‐analysis of 32 studies revealed precipitation changes ranged between +30% and −40%, and temperature changes ranged from +6°C to −3°C for Florida. Seven incremental scenarios were developed at 10% increments in the precipitation change range and nine scenarios with 1°C increments in the temperature change range (driving forces). The causal chains/loops were developed using Driver‐Pressure‐State‐Impact‐Response framework for selected ecosystems and environment (e.g., agroecosystem, mangroves, water resources, and sea turtles) in Florida. The driving force puts pressure on the ecosystem or environment impacting their state, which in turn had a response (e.g., mitigation and adaptation strategies). The framework used indicators selected from studies on climate impact assessments (meta‐analysis and others) for the selected ecosystems as well as author expertise on the topic to develop the chains/loops. The decision tool is applicable to stakeholders and any ecosystem within and outside of Florida.

No abstract

No abstract

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

No abstract