In recent years, numerous climate projections (such as MACA or LOCA) have been made available for use in impact assessments and adaptation planning. However, the breadth of available projections presents a daunting challenge to managers and scientists who are trying to determine which projections are appropriate for a particular decision context.
On getting climate model projections in the hand of managers.
In the past several decades, climate scientists have developed robust models that simulate past climate conditions and provide meaningful projections for the future. In the past several years, researchers have developed downscaled climate projections that provide the kind of local guidance resource managers have been demanding. And now all those managers are making climate-informed decisions.
There is a lot of data out there. It seems like every agency has produced their own downscaled dataset using different methods, training data, and a hodge-podge of global climate models. They are all unique, but none of them are the “best.” This blog post will not give you tips in working downscaled data or picking what is right for your project; my colleague already wrote that post awhile back.
As an isolated island archipelago in the middle of the Pacific Ocean, the Hawaiian Islands have become home to many endemic species found nowhere else in the world. Hawaiʻi provided a unique place for ecological divergence, leading to the evolution of the islands’ expansive and impressive native avifauna. The forest birds in particular are biologically significant to the complex and fragile forest ecosystems of Hawaiʻi.
From a scientific standpoint, Hawaiʻi is a unique location for climate science in the Pacific Island Region. Since climate change is already impacting island nations throughout the region, you could call them the ‘canaries in the coal mines’ that serve as a warning to other areas.
There have been several times so far in my short graduate career where I have ended up arguing with one professor or another over something few would think of. How much does the small stuff matter? That is, how much does a small change in methods in research matter? Let me take a moment to talk about why I think that (at least in the context of climate modeling), the small stuff is very important.
The evening I’m writing this, our first real snow this winter has been on the ground for barely a day. My desk (or rather, kitchen table) is in the watershed of the Cache La Poudre River, at the foothills of the Rocky Mountains of Colorado. The Cache La Poudre is part of the South Platte River which drains to the Platte River, a tributary to the mighty Missouri. The snow is very welcome after a long drought year in the Cache La Poudre, but the drought is still playing out far downstream in the Missouri’s receiving waters, the Mississippi.
The third U.S. National Climate Assessment report, released in early May, provides a national synthesis of climate change and its effects that are already being felt across multiple sectors within the U.S., including coastal flooding and extreme heat in the Northeast, shrinking summer sea ice and thawing permafrost in Alaska, drought and associated increases in wildfires in the southwest, decreased water availability in the Southeast, constrained freshwater supplies in Hawai’i, and changes in streamflow timing in the Northwest.