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How Likely Is a Large Terrestrial Feedback to Climate Change? Mike Goulden et al Transect Surveys Experimental Manipula;on Eddy Covariance Drier Warmer We6er Colder Probability of a change Impact of that change (for example, radiative forcing) Climate ? Weather (means, extremes) Occurrence of fire ? ? Physiology Growth Population size Species reordering ? ? Dominant PFT// biome shift Structure ? Ecosystem goods and services Mass, energy flux in and out (feedbacks) ? Framework for comparing climate feedback risks Analogous to probabilis;c risk assessment – (insurance, engineering, etc) Radiative forcing Probability of Feedback caused by that = X an event risk event • Probability of event – landscape issue that accounts for the frac;on of landscape that is vulnerable (spa;al probability) and the likelihood of an event at a vulnerable loca;on (temporal probability) • An event may have a low probability because only a small frac;on of the landscape is vulnerable or because the event occurs infrequently Increased probability of fire X Radiative forcing caused by a fire = Climate feedback risk via fire • Long-‐term goal to determine rela;ve importance (risk) of various feedbacks -‐ iden;fy, understand, and focus on high probability, high consequence feedbacks GL CSS 1938 1983 1977 2002 California sagebrush, black sage still dominate; Rhus/ Malosma have increased Why so stable? • Differences in soil? Nope – same texture across border – same texture to 2-m depth Loma Ridge - Soil Texture Analysis 80 70 Percentage 60 50 Grassland Shrubland 40 30 20 10 0 Clay Silt Soil Texture Sand • Constant environment? Nope – at least 4 wildfires (1948, 67, 98, 07), an order of magnitude interannual precip variation • Lots of ecological resiliency (at least to normal fire and precip patterns of management since 1930) Resiliency to wildfire – an unfortunate dataset Air T * 65 mph gusts Water input for the various treatments 300 Cumulative ambient Cumulative dry Cumulative wet 1/3 of the plots get extra water 250 1/3 of the plots get normal water 200 150 1/3 of the plots get reduced water 100 50 0 10/1/2008 11/20/2008 1/9/2009 2/28/2009 4/19/2009 6/8/2009 1000 Manipulation 2007 Manipulation 2008 Manipulation 2009 Manipulation 2010 Manipulation 2011 Tustin 1927-2003 Water input (mm) 800 600 400 200 0 0 10 20 30 40 Days with at least 2.54 mm water input 50 N addition Seedling Addition / Sampling Subplots SDL add S R Nbag sRes0 Harv07 Harv07 Harv07 Lit Harv07 07 SDL add Harv09 Harv09 Nbag sRes0 9 9 616/HS Permanent Monitoring Subplots LWS 505 T LWS 229 NDVI 215 416 TDR 616/HS 505 T NDVI 616/HS 616/HS Harv08,0 9 616/HS Seed Addition Subplots Harv08,0 9 TDR 616/HS T T 616/HS 616/HS Harv08 Resin L Resin Harv08 R DOWNHILL S R Tab 7. Timeline and responsibility Task Establish or construct sites Clean up fire debris Repair fire damaged towers Repair damaged Grassland manipulations Repair damaged CSS manipulations Build PJ manipulations Build Pine/Oak manipulations Establish wet side Santa Ana transect Dismantle and restore sites Field manipulations Operate Grassland manipulations Operate CSS manipulations Operate PJ manipulations Operate Pine manipulations Collect and apply seeds Nitrogen fertilization Field measurements GL and CSS plots Field measurements PJ and PO plots Natural Gradient Observations Operate eddy covariance towers Field measurements at tower sites Field measurements at transect sites Data analysis and synthesis Who* 5/1/08 1/1/08 ALL 4 GW 2 GW,MG,UG,CK 44 GW,MG,UG,CK 3333 GW,MG,UG,CK 333 GW,MG,UG,CK 333 AK,UG GW,JS,CK,UG 1/1/09 1/1/10 1/1/11 4/30/13 1/1/13 1/1/12 22 3333333 GW,MG,CK 2222211111222222111112222221111122222211111222222211 GW,MG,CK 211111222222111112222221111122222211111222222211 GW,MG,CK 11111111111111111111111111111111111111111111111111 GW,MG,CK 222211111222222111112222221111122222221111 KS,JS,UG 1111112 1111112 1111112 1111112 1111112 KS,JS,UG 1 1 1 1 1 1 1 1 ALL (mainly JS,CK) 333331111133333311111333333111113333331111133333311 ALL (mainly JS,CK) 22222222222222222222222222222222222222222222222222 GW AK,JS,CK,UG AK,UG MG,KS,AK 111111111111111111111111111111111111111111111111111111111111 2 2 2 2 2 2 2 2 2 22 22 22 22 22 111111111111111111222222222222222222222333333333333333333333 *Tasks performed by the following people (see Budget Justification for details on expertise): MG (Michael Goulden), KS (Katie Suding), GW (Greg Winston), JS (Jane Smith), CK (Chris Kopp), AK (Anne Kelly), UG (Undergraduate Field Assistants), ALL (all members of research team). 4 indicates a very large effort is required (2-3 people full time); 3 indicates a large effort is required (1-2 people full time); 2 indicates a medium effort is required (1-0.5 people full time); 1 indicates a modest or background effort is required 510 hours a week). 2009-10 600 Ambient treatment Dry treatment Wet treatment 400 600 (d) 400 200 200 0 0 (e) (b) 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.6 0.0 (c) (f) 0.6 0.4 0.4 0.2 0.2 0.0 Nov Jan Mar May Nov Jul Date Jan Mar May Jul 0.0 EVI EVI (a) Cumulative water input (mm) Soil water (cm3 cm-3) Soil water (cm3 cm-3) Cumulative water input (mm) 2008-09 Water input 0 100 200 300 400 500 600 700 800 (a) ANPP 600 400 2007 2008 2009 2010 2011 200 0 (b) ANPP 600 400 200 0 0.0 0.2 0.4 0.6 Fraction water increasing species 0.8 1.0 Ripgut Brome (Bromus diandrus) 0.6 0.5 r 0.4 ev o c la n 0.3 io tc ar F 0.2 0.1 0 0 100 200 300 400 500 600 500 600 Water input (mm yr -‐1 ) Filaree (Erodium sp.) 0.6 0.5 r 0.4 e v o cl a 0.3 n o tic ar F 0.2 0.1 0 0 100 200 Water input 300(mm yr -‐1 ) 400 (a) Erodium sps. (d) Lolium multiflorum (g) Bromus diandrus (b) Calandrinia ciliata (e) Lupine sps. (h) Vicia villosa (f) Nasella pulchra (i) Avena sps. 0.4 0.3 0.2 0.1 Fractional species cover 0.0 0.4 0.3 0.2 0.1 0.0 (c) Hirschfeldia incana 0.4 0.3 2008 2009 2010 2011 0.2 0.1 0.0 0 200 400 600 0 200 400 600 Water input (mm) 0 200 400 600 • Rapid changes in relative abundance – Species reordering • Species coexist at climatological precipitation (median 236 mm /yr) • Species reordering tied to traits • Favored with drought - Forbs and shorter time to flowering • Favored in wet treatments - Grasses and N fixers Fractional species cover 0.4 Bromus diandrus Lolium multiflorum Avena sps. Nasella pulchra Calandrinia ciliata Erodium sps. Hirschfeldia incana Vicia villosa Lupine sps. 0.3 0.2 1929-2010 median 236 mm 0.1 0.0 0 100 200 300 Water input (mm yr-1) 400 500 600 • Species coexist at climatological precipitation (median 236 mm /yr) • Grasses have trouble at > ~100 mm • A series of very dry years might eliminate grasses Fractional species cover 0.4 Bromus diandrus Lolium multiflorum Avena sps. Nasella pulchra Calandrinia ciliata Erodium sps. Hirschfeldia incana Vicia villosa Lupine sps. 0.3 0.2 0.1 0.0 0 100 200 300 Water input (mm yr-1) 400 500 600 A consecutive series of unusually wet or dry years is probably necessary to either completely wipe out a PFT or allow a new PFT to establish – this is very infrequent Average 3-‐year precipitation, 1930-‐2010 climate s 600 d o ir e p r 500 a e y-‐ 3 400 m o d n ar 300 0 0 0 0 1 r 200 e p s e c n 100 e r u cc O 3 years with <= 10 cm yr-‐1 0.1 percentile 3 years with >= 50 cm yr-‐1 3.7 percentile 0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 3-‐year mean precipitation (mm) • The dry plots received ~100 mm yr-1 for the first 3 years of the study • These plots became forb dominated • But this was an extreme treatment (<1 percentile) • And the grasses have returned in the most recent 1.5 yrs, which have been wetter than average • Species composition shifts rapidly but changes may be reversible and community type is generally stable • What required to shift community type (for example, CSS to AGS) • CSS plots recovering from 2007 fire – increase in shrubs and decrease in herbaceous plants • Shrubs markedly set back in the dry treatments 100 100 90 90 90 80 80 80 70 70 70 60 60 60 Sage 50 Sage 50 Grass Grass 40 Forb Vine 30 40 Forb Vine 30 Percent Cover 100 Percent Cover Percent Cover -‐H2O, +N Ambient H2O, +N +H2O, +N Grass 40 20 20 10 10 10 2010 Year 2011 Vine 0 0 2009 Forb 30 20 0 Sage 50 2009 2010 Year 2011 2009 2010 Year 2011 Probability of a change Impact of that change (for example, radiative forcing) Climate Very high Weather (means, extremes) Occurrence of fire Moderate Very high Physiology Growth Population size Species reordering Small Very low Dominant PFT// biome shift Structure Large Ecosystem goods and services Mass, energy flux in and out (feedbacks) Small How Likely Is a Large Terrestrial Feedback to Climate Change? • Changes in fire frequency appear likely. But most fire adapted ecosystems recover rapidly from fire, limiting the integrated effects. And the albedo effect may partially or completely offset the carbon effect – a moderately high probability, moderately low consequence mechanism. • Changes in Physiology, Growth, Population sizes, and Species reordering are very likely – these responses are fast – a natural mode of variability. But many of these changes may not have large consequences for climate – a very high probability, low consequence mechanism . • Changes in dominant plant functional type and biome shifts may have larger consequences for climate. But these responses appear less likely – they are slow and very poorly understood – a low probability, very high consequence mechanism.