Planners and natural resource managers use observed climate information at different timescales to help make better decisions. However, there is often a mismatch between the timescales at which managers make decisions and the timescales at which climate models make projections. For example, while a freshwater manager might plan a city’s freshwater infrastructure 10 to 20 years in the future, a climate model provides projections of rainfall that are for the end of the 21st century. While models of climate change at the end of the century are useful for long-term planning and education, it is also helpful for resource managers to have shorter-term climate projections at the seasonal to interannual scale.
Dynamical, or ‘physics-based’ seasonal prediction of precipitation over the Pacific Islands is in its infancy, and the availability of regional climate model predictions for the Pacific Islands region is limited, as many national efforts end at borders of the contiguous 48 states. Because of the small relative size and topographical diversity of Pacific Islands, significant downscaling of global model predictions is needed to make them applicable to island-scale decision making. The main objective for this phase of the research is to perform dynamical downscaled prediction of precipitation at seasonal to interannual timescales (e.g., during different phases of ENSO) at high spatial resolution, starting on the Hawaiian Islands and expanding to US-Affiliated Pacific Islands with stakeholder interest and available resources. Specifically, NOAA operational seasonal prediction model (CFSv2) variables are routinely made available, and they will be downscaled to a resolution that is relevant to island spatial scale (~1 km).The Pacific RISA has previously supported the development of the Hawaiʻi Regional Climate Model (HRCM) by the UH International Pacific Research Center (IPRC). The HRCM is a dynamically downscaled regional model for the Hawaiian Islands at 15 km, 3 km, and 1 km horizontal grid scales. The HRCM has been used to perform a 20-year (1990-2009) present-day climate and projected 20-year (2080-2099) simulations for the late 21st century conditions. One salient result is that both the improved model physics and high model resolutions are needed for realistic simulation of current climate and future projections (Fig. 1).
Figure 1. The observed (a) and simulated (b-c) rainfall over Maui. The future change is shown in (d) and (e).
Fig. 1 compares annual precipitation amounts over the island of Maui simulated with 1 km and 3 km resolutions. Observations show uneven distribution (Fig. 1a) with a peak (> 5000 mm) along the windward side of east Maui and the West Maui mountains, while < 500 mm in the central isthmus. Clearly, the 3 km resolution simulation does not capture this distribution well (Fig. 1c) while the 1 km resolution simulation (Fig. 1b) appears realistic. Thus, this resolution dependency on realistic simulation of current climate is expected to improve the magnitude and distribution of the future projected rainfall change (Figs. 1d-1e). The experience gained from this exercise is vital for the present focus.
To validate the model’s predictive ability, a commonly adopted approach is to ask the model to retrospectively forecast past events (called “hindcasting”), such as the 1982–1983 and 1997–1998 strong El Niño events. Assessing how well different variables in the model hindcast against observed data gives researchers an idea of the model’s ability to predict real-time or future climate. In a recently funded project, the skill of the NOAA operational model (CFSv2) hindcast experiments was assessed. Based on NOAA-recommended metrics, the consensus is that CFSv2 is able to capture modest skill when assessed over the Hawaiian archipelago. Encouraging enough, during strong El Niño events (i.e., 1997-98) the large swings in rainfall and persistence of dryness (from fall through winter and following spring) are skillfully forecast at longer leads by all ensemble members, primarily attributed to realistic representation of physical processes (Annamalai et al. 2014). Due to coarse resolutions employed in CFSv2 model (~125 km), however, the island-scale spatial distribution of precipitation climatology (e.g., Fig. 1a) is not resolved. Keeping in mind the increasing local demands for reliable and future forecast information at island scales, the team will employ HRCM downscaling for seasonal prediction of precipitation.
Research Team H. Annamalai, University of Hawaii Professor of Meteorology and Senior Researcher at IPRC Jan Hafner, Scientific Computer, Programmer, International Pacific Research Institute, University of Hawai‘i Chunxi Zhang, Regional Atmospheric Modeling Specialist, International Pacific Research Institute, University of Hawai‘i
Our Vision
Resilient and sustainable Pacific Island communities using climate information to manage risks and support practical decision-making about climate variability and change.
Dynamical Downscaling in the Hawaiian Islands
Planners and natural resource managers use observed climate information at different timescales to help make better decisions. However, there is often a mismatch between the timescales at which managers make decisions and the timescales at which climate models make projections. For example, while a freshwater manager might plan a city’s freshwater infrastructure 10 to 20 years in the future, a climate model provides projections of rainfall that are for the end of the 21st century. While models of climate change at the end of the century are useful for long-term planning and education, it is also helpful for resource managers to have shorter-term climate projections at the seasonal to interannual scale.
Dynamical, or ‘physics-based’ seasonal prediction of precipitation over the Pacific Islands is in its infancy, and the availability of regional climate model predictions for the Pacific Islands region is limited, as many national efforts end at borders of the contiguous 48 states. Because of the small relative size and topographical diversity of Pacific Islands, significant downscaling of global model predictions is needed to make them applicable to island-scale decision making. The main objective for this phase of the research is to perform dynamical downscaled prediction of precipitation at seasonal to interannual timescales (e.g., during different phases of ENSO) at high spatial resolution, starting on the Hawaiian Islands and expanding to US-Affiliated Pacific Islands with stakeholder interest and available resources. Specifically, NOAA operational seasonal prediction model (CFSv2) variables are routinely made available, and they will be downscaled to a resolution that is relevant to island spatial scale (~1 km).The Pacific RISA has previously supported the development of the Hawaiʻi Regional Climate Model (HRCM) by the UH International Pacific Research Center (IPRC). The HRCM is a dynamically downscaled regional model for the Hawaiian Islands at 15 km, 3 km, and 1 km horizontal grid scales. The HRCM has been used to perform a 20-year (1990-2009) present-day climate and projected 20-year (2080-2099) simulations for the late 21st century conditions. One salient result is that both the improved model physics and high model resolutions are needed for realistic simulation of current climate and future projections (Fig. 1).
Fig. 1 compares annual precipitation amounts over the island of Maui simulated with 1 km and 3 km resolutions. Observations show uneven distribution (Fig. 1a) with a peak (> 5000 mm) along the windward side of east Maui and the West Maui mountains, while < 500 mm in the central isthmus. Clearly, the 3 km resolution simulation does not capture this distribution well (Fig. 1c) while the 1 km resolution simulation (Fig. 1b) appears realistic. Thus, this resolution dependency on realistic simulation of current climate is expected to improve the magnitude and distribution of the future projected rainfall change (Figs. 1d-1e). The experience gained from this exercise is vital for the present focus.
To validate the model’s predictive ability, a commonly adopted approach is to ask the model to retrospectively forecast past events (called “hindcasting”), such as the 1982–1983 and 1997–1998 strong El Niño events. Assessing how well different variables in the model hindcast against observed data gives researchers an idea of the model’s ability to predict real-time or future climate. In a recently funded project, the skill of the NOAA operational model (CFSv2) hindcast experiments was assessed. Based on NOAA-recommended metrics, the consensus is that CFSv2 is able to capture modest skill when assessed over the Hawaiian archipelago. Encouraging enough, during strong El Niño events (i.e., 1997-98) the large swings in rainfall and persistence of dryness (from fall through winter and following spring) are skillfully forecast at longer leads by all ensemble members, primarily attributed to realistic representation of physical processes (Annamalai et al. 2014). Due to coarse resolutions employed in CFSv2 model (~125 km), however, the island-scale spatial distribution of precipitation climatology (e.g., Fig. 1a) is not resolved. Keeping in mind the increasing local demands for reliable and future forecast information at island scales, the team will employ HRCM downscaling for seasonal prediction of precipitation.
Research Team
H. Annamalai, University of Hawaii Professor of Meteorology and Senior Researcher at IPRC
Jan Hafner, Scientific Computer, Programmer, International Pacific Research Institute, University of Hawai‘i
Chunxi Zhang, Regional Atmospheric Modeling Specialist, International Pacific Research Institute, University of Hawai‘i
Our Vision
Resilient and sustainable Pacific Island communities using climate information to manage risks and support practical decision-making about climate variability and change.
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