Regional Ecosystem Dynamics and Climate Feedbacks

A. David McGuireTed SchuurScott Rupp

Helene GenetEugenie Euskirchen

BNZ Annual Symposium20 February 2015

Regional Ecosystem Dynamics and Climate Feedbacks Questions

(1) How will interactive responses of disturbance regimes, ecosystem structure/function, and successional pathways to future climate variability and change influence regional ecosystem dynamics?

(2) How will projections of regional ecosystem dynamics affect regional energy and water feedbacks to the climate system?

(3) How will projections of regional ecosystem dynamics affect regional CO2 and CH4 feedbacks to the climate system?

Tasks

(1) Couple model of fire regime (ALFRESCO) with model of ecosystem structure and function (DVM-DOS-TEM), incorporate information from empirical studies, and conduct retrospective analysis of the coupled model framework;

(2) Apply the coupled model for future scenarios of climate and analyze changes in ecosystem function/structure at the regional scale;

(3) Analyze water and energy feedbacks among the applications;(4) Conduct factorial experiments for future scenarios of climate

change and evaluate effects of climate and disturbance on estimates of CO2 and CH4

Primary Support for this ResearchIdentifying Indicators of State Change and Predicting

Future Vulnerability of Alaska’s Boreal Forest (funded by DoD SERDP)

Modeling Objective: Develop models that can forecast landscape change in response to projected changes in climate, fire regime, and fire management.

Integrated Ecosystem Model for Alaska and Northwest Canada (funded by USGS and Alaska Landscape Conservation Cooperatives

To develop a conceptual framework for integrating important components of an ecosystem model for Alaska and Northwest Canada including: fire, vegetation dynamics/succession, biogeochemistry permafrost dynamics, and hydrology.

Primary Support for this Research (cont.)Alaska Land Carbon Assessment (funded by USGS)Objectives are to assess: 1) the amount of C stored in ecosystems

of Alaska 2) the capacity of Alaska ecosystems to sequester C, and 3) evaluation of the effects of the driving forces such as climate and wildfire that control ecosystem C balance.

Permafrost Carbon Network (funded by NSF)Address the question “What is the magnitude, timing, and form

of the permafrost carbon release to the atmosphere in a warmer world?” through synthesis by linking biological C cycle research in the permafrost region with well-developed networks in the physical sciences focused on the thermal state of permafrost.

Morning Session (Friday, 20 February 2015): Progress and Future Directions of

Climate Feedbacks Research

8:30 – 8:50 Climate Change and the Permafrost Carbon Feedback – Ted Schuur 8:50 – 9:10 Projections of Climate and Vegetation Change – Scott Rupp 9:10 – 9:30 Consequences for Carbon Feedbacks – Helene Genet 9:30 – 9:50 Consequences for Water/Energy Feedbacks – Eugenie Euskirchen 9:50 – 10:10 Coffee Break10:10 – 10:30 Future Directions for Climate Feedbacks Research – Dave McGuire10:30 – 11:00 Discussion of Future Directions for Climate Feedbacks Research11:00 – 11:15 Overview of Proposal Development – Roger Ruess 11:15 – 12:00 Theme highlights and directions – Other Theme Leaders

12:00 – 1:30 Lunch

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(1) Substantial progress on the effects of climate, fire, and top down permafrost thaw in uplands on vegetation dynamics and biogeochemistry. Still work to do (such as insects and pathogens).

(2) Begun modeling research on the effects of thermokarst on wetland vegetation dynamics and biogeochemistry.

(3) Need to begin modeling research on biogeochemical linkages of uplands and wetlands to inland surface waters (lakes and streams) and biogeochemistry of inland surface waters.

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(1) Substantial progress on the effects of climate, fire, and top down permafrost thaw in uplands on vegetation dynamics and biogeochemistry. Still work to do.– Finish two-way coupling of ALFRESCO with DVM-

DOS-TEM and GIPL – Additional research on modeling successional

trajectories– Begin research on modeling the dynamics and

effects of insect and pathogen disturbances

ALFRESCO GIPLDVM-DOS-TEM

Burned area

O Horizon Thickness

Vegetation Type Soil Thermal Profile

Vegetation Carbon

Soil Moisture Profile

Moss Thickness

Vegetation Canopy

Elevation, Slope, Aspect. Historical Fire Mineral Soil Texture

Air Temperature, Precipitation, Initial VegetationSnow Water Equivalent

Finish two-way coupling of ALFRESCO with DVM-DOS-TEM and GIPL

Additional Research on Modeling Successional Trajectories.

(Johnstone et al. 2010)

Relative dominance of spruce in post-fire recruitment is related to fire severity, pre-fire stand age and drainage conditions.

The drivers of post-fire recruitment composition

Fire severity Drainage Stand age

Johnstone et al. 2010

Drainage

Pre-fire vegetation (PFV)Mesic

SubhygricFailure

High/moderate

lowMixed trajectory

Pre-fire stand ageXeric Pre-fire vegetation (PFV)

< 50 yr

> 50 yrFailure

Mixed trajectory

high

low/moderate

Deciduous trajectory

Pre-fire stand age

Thermokarst ?

< 50 yr

> 50 yrFailure

Evergreen trajectory

PFV = decid.Deciduous trajectory

Fire severityPFV = everg.

PFV = decid.Deciduous

Fire severityPFV = everg.

Thermokarst ?

Regression Tree Approach to Modeling Dynamics of Post-fire Recruitment and Successional Trajectories : Helene Genet

Build Successional Dynamics into DVM – DOS – TEM

(Euskirchen et al. 2009)

The dynamic vegetation model simulates carbon and nitrogen dynamic of various plant functional types, competing for light and nitrogen uptake.

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(1) Substantial progress on the effects of climate, fire, and top down permafrost thaw in uplands on vegetation dynamics and biogeochemistry. Still work to do.– Finish two-way coupling of ALFRESCO with DVM-

DOS-TEM and GIPL – Additional research on modeling successional

trajectories– Begin research on modeling the dynamics and

effects of insect and pathogen disturbances

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(2) Begun modeling research on the effects of thermokarst on wetland vegetation dynamics and biogeochemistry.– Development of Alaska Thermokarst Model (ATM)– Development of Peatland DOS-TEM/DVM-DOS-TEM– Coupling of ATM with Peatland DVM-DOS-TEM

Quantifying the Area Susceptible to ThermokarstFunction of ice content, landscape position (lowlands), presence of

peat (histels), and presence of permafrost(from Helene Genet)

Development of the Alaska Thermokarst Model

Conceptual Model of Landscape Change Associated with

Thermokarst Disturbance

PermafrostPlateau

Thermokarst Lake

Young Fen(< ~100 yr old)

Young Bog( < 100 yr old)

FenL

Old Fen Old Bog

Thermokarst-Prone Landscape Change Questions

• What is the current distribution of land cover types in thermokarst-prone landscapes?

• What are the transition rates among land cover types during the satellite era?

• What are the controls over transition rates (e.g., climate, permafrost, hydrology)?

• How is the distribution of land cover types projected to change in the future?

Biogeochemistry Change Questionsin Thermokarst-Prone Landscapes

• How do land cover transitions in thermokarst-prone landscapes (and associated changes in permafrost and hydrology) influence carbon storage and fluxes in land cover types?

• How do land cover transitions (and associated changes in permafrost and hydrology) influence the loading of carbon into lake and stream networks ?

• How will climate change (and associated changes in land cover transitions, permafrost, and hydrology) influence carbon dynamics in lakes, stream networks, and wetland complexes?

PERM Region 05B: Discontinuous Boreal Permafrost

Bookkeeping Model: Estimates of Carbon Dynamics of each Land Cover Type

(Jen Harden, Jon O’Donnell, Others)Collapse Bogs (CB)

Delta Shallow SOC (g C m-2 yr-1)

Delta Deep SOC (g C m-2 yr-1)

Delta VEGC (g C m-2 yr-1)

0-50 128 -1085 051-100 108 -488 0

100-500 56 -50 0>500 13 0 0

Permafrost Plateau Forests (PPF) 38 14 0

Treed Bogs (TB) 13 0 0

Net CH4 Emissions(g C m-2 yr-1)

NEE (+ to atmosphere) (g C m-2 yr-1)

DOC Flux(g C m-2 yr-1)

Collapse Fens (CF) 6 -8 2.5

Thermokarst Lakes (TL) 4.6 15 2.5

Bookkeeping Model: Transition Rates(Proportion Per Year)

From/To (Proportion)

Permafrost Plateau Forest

Thermokarst Lake Collapse Fen Collapse Bog Treed Bog

Permafrost Plateau Forest (PPF) NAN 0.0007619 0.0042418 0.0001359 NANThermokarst Lake (TL) NAN NAN 0.0012821 NAN NAN

Collapse Fen (CF) NAN NAN NAN 0.0002 NAN

Collapse Bog (CB) NAN NAN NAN NAN 0.0001

Treed Bog (TB) 0 NAN NAN NAN NAN

Bookkeeping Model: Dynamics of Thermokarst Land Cover Types 1950-2300 Using

Contemporary Transition Rates

Bookkeeping Model: Consequences of Using Contemporary Transition Rates for

Cumulative Changes in Carbon Storage

Raised WT

Lowered WT

Control

Development of Peatland DOS-TEM/DVM-DOS-TEM has relied on data from The Alaska Peatland Experiment (APEX)

Peatland-DOS-TEM Model (Fan et al. 2013)

No historical fireIn APEX sites

Peatland Model (Fan et al. 2013)

A. 1901-2011 B2. 2012-2099 (GFDL) A1B A2 B1

4.5

199 5.7

209 CH4 efflux

HR CO2 efflux

Litterfall C

Soil OC change

SimTP

-2.3

259 5.8

263

Sim0P

9.5

235 6.0

250

SimT0

-5.5

226 4.7

225

Sim00

10.1

234 5.8

250

SimTP

0.6

250 5.8

256

Sim0P

9.4

233 6.0

249

SimT0

-3.0

227 5.1

229

Sim00

9.8

233 5.8

248

SimTP

8.8

251 5.8

266

Sim0P

10.9

242 6.3

259

SimT0

0.7

224 5.3

230

Sim00

11.2

241 6.1

259

from Fan et al. 2013(units are g C m-2 yr-1)

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(2) Begun modeling research on the effects of thermokarst on wetland vegetation dynamics and biogeochemistry.– Development of Alaska Thermokarst Model (ATM)– Development of Peatland DOS-TEM/DVM-DOS-TEM

Zhaosheng Fan (now DOE-ANL): FenYanjiao Mi: Collapse Scar Bog, Permafrost Plateau, and

biogeochemical consequences of thermokast disturbance based on data from chronosequence studies.

– Coupling of ATM with Peatland DVM-DOS-TEMThis is an activity of the IEM team (science question: How does coupling (internal feedbacks of the system) influence wetland dynamics and biogeochemistry?)

Future Directions of BNZ Regional Climate Feedbacks Research

(Dave McGuire Perspective)

(3) Need to begin modeling research on biogeochemical linkages of uplands and wetlands to inland surface waters (lakes and streams) and biogeochemistry of inland surface waters.

– Mack et al. DOE proposal in review– TEM6 models DOC and DON loading

Addressing Uncertainties

in BNZ Research

Afternoon Session (Friday, 20 February 2015): Quantifying Uncertainty and Understand

Legacies

1:30 – 1:35 Overview of the Afternoon Session – Dave McGuire 1:35 – 1:50 Analyzing Uncertainty in Field/Experimental Research: A

Hierarchical Bayesian Perspective – Colin Tucker 1:50 – 2:05 Role of Model-Data Fusion in Analyzing/Quantifying Uncertainty

– Dave McGuire 2:05 – 2:20 Characterizing Uncertainty in Applications of Ecological Models –

Jeremy Littell 2:20 – 2:40 Perspectives on Studying the Role of Legacies in BNZ Research –

Michelle Mack 2:40 – 3:00 General Discussion

3:00 – 3:15 Coffee Break

Role of Model-Data Fusion in Analyzing/Quantifying Uncertainty

(D. McGuire and H. Genet)

• Sources of Uncertainty in Models• Traditional Parameter Uncertainty Analysis• Model-Data Fusion

Sources of Uncertainty in Modeling

• Conceptual Uncertainty– Compare dynamics of alternative models

• Formulation Uncertainty (equations)– Compare models with different equations

• Parameter Uncertainty– Various approaches to analyzing uncertainty

• Application Uncertainty– Jeremy Littell’s presentation

Parameter Estimation

• Literature-based estimation• Experimentally based estimation• Calibration

Traditional Parameter

Uncertainty Analysis• Definition

Relates the variability of model predictions to uncertainty in parameter estimates.

Uncertainty analysis is analyzing the effect of the variance of a parameter to the model predictions.

Error Propagation• Amplification• Compensation

Error amplified : σz > σx Error compensated: σz < σx

Multiple parameter uncertainty analyses:100 independent sets of parameters value using Monte Carlo iterations applied to 43 parameters varying within an “acceptable” range, assuming a uniform probability density function for each parameter.

SWHi = Threshold snow water equivalent at which forage intake goes to zero for each ungulate category ; GWP = Water content per kg protein ; FGB = Forage intake per kg of body mass ; BMi = initial body mass for each ungulate category; PSN(24) = Snow depth in unburned forest. From Turner et al. (Ecological Applications).

Model-Data Fusion• Model and data integration, also called model–data fusion or model–data synthesis, is

defined as combining models and observations by varying some properties of the model, to give the optimal combination of both (Raupach et al., 2005). Model–data fusion encompasses both calibration and data assimilation.

• Model–data fusion can be characterized as both an inverse problem, analyzing a system from observations, and as statistical estimation.

• Calibration: Parameter estimation to produce desired outputs for a given input.

• Data Assimilation: observations are used to refine estimates of the evolving model state.

• Model–data fusion brings together four components: – external forcing, – a model that relates model parameters, state and external forcing to observations, – observations, and – an optimization technique.

Example: Uncertainty in the fate of soil organic carbon: A comparison of three conceptually different decomposition models in a larch plantation (He et al. JGR-B in press)

• Compared three structurally different soil carbon (C) decomposition models (one driven by Q10 and two microbial models of different complexity)

• The models were calibrated and validated using four years of measurements of heterotrophic soil CO2 efflux from trenched plots in a Dahurian larch (Larix gmelinii Rupr.) plantation.

• Parameters in each model estimated using a Bayesian data assimilation framework

Model Validation

Differences in Model Dynamics and Uncertainty

Simulated 100 years responses of SOC stock for the three models. Top panel (a-c) is trenched plot simulation; bottom two panels (d-i) are model simulations under 4.8 °C progressive increasing soil temperature and litterfall. The deep blue and red lines (for 3-pool Q10 model) represent ensemble mean from the 100 independent optimization runs for each model, the light colored lines are the results from each ensemble member.

Afternoon Session (Friday, 20 February 2015): How should BNZ quantify uncertainties

and understand legacies?

3:15 – 3:30 Organization of and charge to working groups and discussion points

3:30 – 4:30 Working groups convene 4:30 – 5:30 Working Group Reports 5:30 – 7:00 Mixer, Dinner, Poster Session

Charge to Working Groups

Data and Experimental Uncertainty Group:• How do we quantify uncertainty across the extended

site network?

Model Uncertainty Group:• What aspects of uncertainty should/can be

addressed?• What approaches should be used to addressed

understand/quantify parameter-based uncertainty?• Which data from the extended site network can be

used to address uncertainty?

Legacy Group:

Morning Session (Saturday, 21 February 2015):

BNZ Management and Outreach 8:30 – 9:30 BNZ Site Management – Discussion re gearing up the ESN – Jamie Hollingsworth 9:30 – 9:50 Progress and Directions of BNZ Information Management – Jason Downing 9:50 – 10:15 Progress and Directions of BNZ Education and Outreach – Elena Sparrow 10:15 – 10:30 Coffee Break 10:30 – 10:45 USDA Forest Service Perspective on BNZ LTER – Paul Anderson10:45 – 11:00 LTER Citizen Science – Christa Mulder and Katie Spellman11:00 – 11:15 ITOC – Mary Beth Leigh11:15 – 11:45 Discussion of the Future Directions of BNZ Outreach – Marie Thoms and Alison York 11:45 – 12:00 Organization of and Charge to Afternoon Working Groups – Roger Ruess 12:00 – 1:30 Lunch

Afternoon Session (Saturday, 21 February 2015): Next Steps in Developing the BNZ Renewal

Proposal

1:30 – 3:00 Theme Working Groups Convene 3:00 – 3:15 Coffee Break 3:15 – 4:30 Reports from Theme Working Groups 4:30 – 5:00 General Discussion on Next Steps 5:00 Adjourn