Climate variability and change in the Canadian Eastern Subarctic IRIS region (Chapter 2)

Transcription

1 Climate variability and change in the Canadian Eastern Subarctic IRIS region (Chapter 2) Lead authors 1 Ross Brown and 2 Mickaël Lemay Contributing Authors 2 Carl Barrette, 2 Michel Allard, 2 Yves Bégin, 3 Trevor Bell, 4 Monique Bernier, 4 Stéphanie Bleau, 5 Diane Chaumont, 5 Anne Frigon, 5 Dominique Paquin 1 Environment Canada, 2 Center for Northern Studies (CEN), 3 Memorial University, 4 INRS- ETE, 5 Ouranos The photo contributors to the draft IRIS4 report are acknowledged for the numerous photos taken from the report included in this presentation!!! Environment Canada Environnement Canada

2 Outline: Overview of revised Chapter outline Key concepts and messages Conclusions - implications for impacts and adaptation

3 Revised Chapter Outline (December 2010) 2.1 Introduction - purpose of chapter, importance of climate, outline of contents (complete climate scenario material provided in Appendix at end of chapter) 2.2 The climate of Nunavik and Nunatsiavut - what drives and defines the climate of the region and its variability - importance of climate as a resource (e.g. abundant stored water for hydroelectricity generation) and role in natural and human systems - highlight links to climate-sensitivities identified in other chapters 2.3 Climate observations and information - brief overview of sources of climate data and information (e.g. climate stations, satellite obs, reanalyses, proxies, indicators, traditional knowledge) and their limitations - key concepts to convey are: discontinuous nature of obs, uneven spatial distribution, biases and errors

4 Revised Chapter Outline (December 2010) contd. 2.4 Observed variability and trends in key climate variables and indicators Air temperature (and derived indicators e.g. start/end of freeze period) Precipitation Snow cover Ice cover Glaciers Permafrost as a climate indicator Proxy climate information (e.g. tree rings) 2.5 Climate change projections - summary and discussion of the material contained in the Appendix with respect to the climate sensitivities identified in the other chapters 2.6. Conclusions Key findings, implications for decision making Appendix 1 - Climate Change Projections for Nunavut and Nunatsiavut for 2050

5 Key Message #1: Climate is important and a resource! Climate defines the environment! e.g. treeline, vegetation types, permafrost distribution, snow and ice cover duration, snow depth, ice thickness, active layer depth, animal habitat etc. Climate is a resource! Sustains ecosystem services including transport, hunting and hydroelectric power production. Ecosystem services have different sensitivities to climate variability and change Climate varies! Climate varies daily, seasonally, from year-to-year and from decade-to-decade and includes extreme events.

6 The climate of Nunavik-Nunatsiavut exhibits strong gradients over local and regional scales Mean snow cover duration, (days) Mean March snow water equivalent, (mm) Mean number of days with snow cover 210 Regional gradients in snow cover duration and snow accumulation driven by temperature gradients and proximity to preferred storm tracks 120 Montreal: ~120 days snow cover Variation in snow water equivalent along a 1.7 km transect near Puvirnituq (P. Toose, Env. Canada)

7 Key Message #2: Documenting the current state of the climate of Nunavik-Nunatsiavut is a major challenge Surface climate observing networks have major gaps over northern regions, and long term stations are mainly located in coastal communities. Snow course network

8 The climate station network may not be capturing what is actually going on over the larger region gridded datasets such as CANGRID and CRU are interpolating information from coastal stations 2003 MODIS CANGRID Mean annual air temp for 2003 from CANGRID (Milewska et al., 2005) Mean annual temperature for 2003 estimate from MODIS satellite data (Hachem et al., 2009, Perm. Periglac. Proc., 20, Gridded station data has interior 2-4 C warmer than satellite estimates (effect of lake ice and residual snow patches)

9 Ar c h i v e s Tree-ring network 9 9

10 Temperature ( o C) Summer Max. temperatures (June - August) (Multiple regression models, ANN / bootstrap error assessment) Observed values Years Estimated values Nicault, Bégin et groupe ARCHIVES Able to reconstruct ~10 variables from analysis of tree-rings and isotopes Potential to extend time series back 1300 years using fossil trees collected from subarctic lakes

11 gridded reanalysis products such as ERA-40 and NCEP have coarse resolution and have not been evaluated in detail over the IRIS4 region satellite datasets such as snow cover extent are affected by cloud cover, forest, and have limited periods of data availability integrating traditional knowledge is a challenge often a miss-match in the scales of information required by users and the available data sources Temporal coverage of datasets with gridded snow cover information NOAA NCEP ERA -40 ERA-40 rec CCRS PMW MODIS QSCAT IMS-24 IMS-4 CMC

12 Key Message #3: The climate of the Eastern Subarctic IRIS region is strongly affected by natural variations in atmospheric and oceanic circulation from annual to decadal to multidecadal time scales

13 The North Atlantic Oscillation (NAO) exerts major controls on interannual variability in winter air temperature and precipitation over the IRIS4 region. Nunavatsiut and eastern Nunavik: + NAO = Cold, dry winter NAO = Warm, high precip winter Jones et al. NAO index ( Correlation of mean winter air temperature (left) and precipitation rate (right) from the NCEP Reanalysis with corresponding NAO index over the period Maps generated using NOAA online plotting tools at

14 The combined influences of atmospheric and oceanic variability are evident in regionally-average snowfall series Regionally-averaged Annual Snowfall Anomaly Series 400 Annual snowfall anomaly wrt (cm) Nunatsiavut (3 stns) Nunavik (4 stns) -300 R. Brown, Env. Canada

15 Key Message #4: Abrupt recent warming (from 1993) is generating a rapid response in the climate and environment of Nunavik Nunatsiavut Air temperature ( C) Nain Kuujjuarapik Kuujjuaq Kangiqsualujjuaq Iqaluit Coral Harbour Year From chap 8

16 Increase in number of thermokarst lakes (chap 6) Rapid changes in vegetation (chap 8) % decrease in glacier area between (chap 2) Earlier melt of snow and ice; more dangerous ice conditions (chap 2 5) Change in traditional species habitat and abundance (chap 7 9)

17 For example, snow is melting 3-4 weeks earlier than it did 30 years ago and is taking 1-2 weeks longer to form a continuous snow cover in the early winter period 40 Annual variation in spring snow cover duration anomaly (days) Kuujjuaq A. Nunavik avg (NOAA satellite) R. Brown, Env. Canada Note that the reductions in snow cover duration kicked in well before 1993 an interesting research project to understand what is driving these changes!

18 Glaciers are precarious micro climatic features of the coastal region of the Torngat Mountains and sensitive indicators of change depend on local shading for their existence First systematic assessment of glacier extent with a preliminary estimate of glacier change from repeat surveying in 2005 (aerial photography) and 2007 (SPOT5 HRS satellite imagery) by Barrand et al. (2010). Year Number of Glaciers Size range (km 2 ) Total area (km 2 ) to to Superguksoak Glacier in 2005: the white line outlines the 2005 margin, the red line the 2007 margin. Source: Bell et al. (2010) 83% of the glaciers experienced an areal decrease over

19 Abrupt decrease in ice cover duration by ~20 days after 1993 according to simplified ice model driven with ERA-40 reanalysis air temperature and precipitation ( ) 270 Ice cover duration (days) Simulated lake ice cover duration over Nunavik from ERA R. Brown, Env. Canada

20 Key Message #5: There is more to climate scenario construction than cranking-up a climate model there are major sources of uncertainty that need to be understood

21 Development of climate scenarios requires several steps: Statistical downscaling Dynamical downscaling with RCM Source: Regionalization of climate change information for impact assessment and adaptation, Filippo Giorgi

22 This process involves four main sources of uncertainty: 4 th IPCC Assessment 1. Uncertainty in how future GHG emissions will evolve 4 th IPCC Assessment 2. Differences between GCMs (physics, feedbacks etc) 3. The internal variability of the climate system simulated by climate models R. de Elia, Ouranos 4. The downscaling method F. Giorgi, WMO Bulletin

23 Recommended Strategy: where possible use multiple GCMs for different emission scenario to bracket the uncertainties Projected changes in mean annual air temperature ( C) and total precipitation (%) for 2050 over the Nunavik and Nunatsiavut regions. Runs in color used to generate scenarios for Nunavut-Nunatsiavut

24 The consistency in projected changes varies seasonally and by variable 6 CRCM runs, 2050, A2 scenario 6 CRCM runs, 2050, A2 scenario Seasonal characteristics of projected change in monthly mean temperature (left panel) and total precipitation (right panel) from six CRCM runs for 2050, for all the grid cells of the study region. The outer lines represent the range in the six simulations. (C. Barrette, U. Laval)

25 Able to get an idea of the spatial variation in uncertainty by mapping the standard deviation of different runs Average projected change in mean annual air temperature ( C) for 2050 and standard deviation from six CRCM runs Std/Delta ~ 0.15 Average projected change in annual maximum snow depth (%) for 2050 and standard deviation from six CRCM runs Std/Delta ~ 1.0 Maps generated by Carl Barrette, U. Laval

26 Key Message #6: There are important season and spatial differences in projected climate changes over Nunavik-Nunatsiavut

27 Maps and summary table provided for projected changes in 12 climate variables/indicators for 2050 selected for their relevance to documented climate sensitivities related to Permafrost, Vegetation, Fresh Water, Arctic Charr, and Caribou (sample below for 3 variables) Nunavik Nunatsiavut Climate variables Projected change over region Comments Projected change over region Comments Growing degree-days Start of freeze period Mean snow depth 50 to 150% increase Largest changes over northeastern Ungava; smallest change over southwest region around James Bay 8 to 19 days later Largest change over southwest region around James Bay and Hudson Bay coast; smallest change over Ungava Peninsula -7 to +7% Decreases dominate with one area of increase over northern Ungava Peninsula; largest decreases over southern and eastern regions 50 to 130% increase Largest changes over Torngat Mountains; smallest changes around Lake Melville 9 to 12 days later Largest changes in southern part of region -10 to -20% Largest decreases over southeast coastal region.

28 The spatial pattern of projected climate changes varies across the region for most variables the largest changes are projected to occur over northern regions Winter (Oct-Apr) Summer (May-Sep) Maps generated by Carl Barrette, U. Laval Average projected change in seasonally-averaged mean air temperature ( C) for 2050 from six CRCM runs assuming the A2 emission scenario

29 Projected 10-25% increases in precipitation with the largest increases in the northwest. Projected % change in total annual precipitation for 2050 from 6 different runs of the Canadian Regional Climate model assuming the SRES A2 emission scenario. Map generated by Carl Barrette, U. Laval

30 Conclusions - implications for impacts and adaptation The climate of the IRIS4 region is characterized by important gradients and by large natural climate variability The response of the region to global climate warming is shown to exhibit important seasonal and spatial differences with the largest changes (and largest uncertainties) over more northern areas Our ability to describe and monitor the climate of Nunavik and Nunatsiavaut is limited in many areas Major role for enhanced environmental monitoring and prediction e.g. new satellite products, high resolution reanalyses and regional climate models Provision of locally-relevant information for planning and decisionmaking will in many cases require customized approaches developed in consultation with user groups and incorporating local knowledge Ouranos has been providing these customized services to clients for the past 5 years.

31 Nakurmimarialuk! Merci! Thank you!