Integrating Social with Natural Science Needs and some practical methods John Walsh & Larry Hamilton ARCSS workshop, Seattle 4/2007

Some important scientific challenges in the Arctic: -- nature of local changes -- fate of sea ice -- trajectory of Arctic hydrologic cycle

“The weather seems less stable and predictable” “From sources of indigenous knowledge across the Arctic come reports that the weather seems more variable, unfamiliar, and is behaving unexpectedly and outside the norm. Experienced hunters and elders who could predict the weather using traditional techniques are now frequently unable to do so. Storms often occur without warning. Wind direction changes suddenly... As noted by several elders, ‘the weather today is harder to know.’” Arctic Climate Impact Assessment, Overview Report, p. 97

Yearly storm counts at Barrow, Alaska [from D. Atkinson, UAF]

Key needs: Integration of indigenous and instrumental information New measures (combinations of variables?) New diagnostic approaches

We know that sea ice coverage has decreased over the last few decades

IPCC models: Arctic sea ice coverage, [from P. Prestrud, CICERO, Oslo]

[from Polyakov et al., 2006]

Key needs: Integrated databases (ice, ocean, atmosphere…) for diagnosis of recent changes Sea ice products that better suit user needs Better definition of sea ice constraints on species (e.g., marine mammals), ecosystems, human activities Narrowing of uncertainty of projected changes

How will the Arctic’s hydrologic cycle change?

Drying of Arctic lakes [from L. Hinzman et al.]

Arctic hydrologic cycle appears to be accelerating [Peterson et al., 2006]

Precipitation changes (mm/day) projected by climate models ( ) to ( ) mm/day WinterSummer

Needed: An integration of hydrologic information

An integration approach to meet needs: Data assimilation (ingestion of observations into models) for reconstruction of temporal evolution of 3-d system -- attempted for: atmosphere, sea ice, upper ocean -- near-term possibilities: hydrology, upper soil -- longer-term: biogeochemistry, ecosystems, …

Time and space as integrating dimensions

Time as an integrating dimension: Yearly time series of salinity, fish catch, and the population of two Iceland towns.

A story of climate, fish and people, from “Rise and fall of the herring towns”

Time series allow integration of quantitative data from different disciplines. They also create frameworks for integrating qualitative data such as local knowledge. “I remember well the years after 1950, the talk — mother and father talked about the families that had moved away. It was sad. Things slowed down. The people who thought they had the best opportunities — had more skills, education, connections, family in other areas — were more likely to leave. There was a ‘brain drain’ as the educated went off to study, and did not come back.” Siglufjörður native, on the town’s decline after herring “In the winter of ’67 – ’68 I remember watching the sea ice filling the fjord. No one liked the ice. Many had terrible experience of the ice years earlier in their lives with cold, isolation and even starvation. The creaky sound of the blocks rubbing together and the icy stale air that surrounded the seaside still remains a vague childhood memory in my head.” Eastfjords resident, recalling an ice winter that ended the era

Modeling with integrated time series — Daily ski-area attendance during one season, graphed with snowdepth in city and mountains. “Ski areas, weather and climate”

Data and model predictions of daily attendance at another ski area, through 9 winter seasons. From “Ski areas, weather and climate.”

Space as an integrating dimension: Arctic RIMS website integrates climate and hydrology data for 25 km square EASE grid cells covering the pan-Arctic.

Political divisions such as boroughs of Alaska can be mapped onto the same grid-cell framework (H3L project).

We can integrate variables from both social and natural-science domains, such as trends in temperature and population (H3L).

By doing this we make a multivariate human-dimensions layer, spatially linked to climate and hydrology layers (H3L).

Population change vs. primary and tertiary-sector employment

Declining regions tend to have older populations

Percent of population 65 and older across the pan-Arctic

Change in pan-Arctic populations vs. temperature trends (H3L)

THANKS