Quantifying and Valuating Radiative Forcing of Land-Use Changes from Potential

Quantifying and Valuating Radiative Forcing of Land-use Changes from Potential

Forestry Activities across the Globe

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Dan Liu

Graduate Program in Environmental Science

The Ohio State University

2018

Master’s Examination Committee

Dr. Kaiguang Zhao, Advisor

Dr. Karen Mancl

Dr. Brent Sohngen

Copyrighted by

Dan Liu

2018

Abstract

Climate mitigation strategies to combat global warming recognize the roles of terrestrial ecosystems in sequestering carbon and lowering atmospheric CO2 levels. Reducing forest loss and improving forest management have been widely acknowledged as effective measures to improve climate benefits. However, forestry activities and land use changes, such as deforestation, reforestation and afforestation, will not only change biological carbon uptake but alter land biophysical processes concerning evapotranspiration, surface albedo, land surface roughness, land-air interactions, all of which affect climate. Forestry activities, though enhancing carbon sequestration, may have negative climatic consequences and further accelerate global warming. Understanding climate response to forestry activities and their biogeochemical and biophysical mechanisms is important in formulating policies to optimize climate benefits of forestry or land management activities.

This work seeks to quantify the biophysical forcing and climatic impacts of land use and land cover changes across the entire globe through the combined use of remotely sensed observations and climate model simulations. The emphasis is on albedo (i.e., how much sunlight is reflect back) and radiative forcing (i.e., how much more energy is kept or lost in the climate system for a given land change). The focus is on four vegetation types, ii

evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), grasslands (GRA), and croplands (CRO). Surface albedo was compared among the four vegetation types.

Radiative forcingwas calculated for potential land conversions from GRA or CRO to ENF or DBF. The shadow price of carbon and albedo was estimated using DICE model as a first-order approximation of economic benefits or losses associated with biophysical climate regulation of land management activities.

Forests generally have lower albedo than adjacent grasslands or croplands, particularly where snow is frequent. Consequently, forests tend to induce positive radiative forcing, counteracting the cooling effect of carbon sequestration. The global mean albedo-induced radiative forcing was 1.53 W/m2, 0.61 W/m2, 0.74 W/m2, and 0.58 W/m2 for land conversion from GRA to ENF, GRA to DBF, CRO to ENF, and CRO to DBF, respectively.

Planting deciduous broadleaf trees, therefore, is more likely to produce cooling benefits than evergreen needleleaf trees. The global social cost of carbon and radiative forcing for

-3 -1 -1 2020 is $37.25 per ton of CO2, and 1.01×10 $w yr , respectively.

Overall, forests exert positive radiative forcing by reducing surface albedo, thereby dampening the climate benefits from carbon sequestration and stressing the importance of incorporating biophysical factors into climate mitigation strategies. This work also demonstrates the utility of climate-economic model in supporting land and ecosystem management, and climate change research.

iii

Dedication

Dedicated to my families and friends

Acknowledgments

First and foremost, I would like to express my appreciation to my advisor, Dr. Kaiguang

Zhao, for his endless hours of support and guidance. I would also like to thank the members of my examination committee, Dr. Karen Mancl and Dr. Brent Sohngen. Their expertise, patience, and vast knowledge contributed significantly to my thesis experience.

I am grateful to Dr. Alexis Londo and my colleagues, and also want to thank all the members of the Environmental Science Graduate Program and the School of Environment and Natural Resources for their patience, guidance, and assistance.

I would like to thank my dear family for their support and understanding during the stressful intense time prior to my graduation. Lastly, thanks to all of people who have assisted me during my time here in Ohio.

Vita

2009……………………………………... B.S (Agriculture), Landscape Architecture,

China Agricultural University

2013……………………………………... M.LA. Landscape Architecture, China

Agricultural University

2006……………………………………... Graduate Research Assistant, SENR, The

Ohio State University

2017 - present…………………………… Graduate Teaching Assistant, SENR, The

Ohio State University

Fields of Study

Major Field: Environmental Science

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vi

Table of Contents ...... vii

List of Tables ...... x

List of Figures ...... xi

Chapter 1 Introduction ...... 1

1.1 Climate-regulation Services ...... 1

1.2 Carbon Sequestration and Storage ...... 3

1.3 Surface Energy Fluxes ...... 5

1.3.1 Albedo and global warming ...... 5

1.3.2 Land surface temperature ...... 9

1.4 Hydrological Cycle ...... 11

vii

1.5 Objectives and Framework ...... 12

Chapter 2 Biophysical forcing from potential forestry activities...... 15

2.1 Introduction ...... 15

2.2 Data and Method ...... 20

2.2.1 Data ...... 20

2.2.2 Comparisons of surface albedo between contrasting vegetation ...... 22

2.2.3 Potential land conversions between vegetation ...... 24

2.2.4 Shortwave radiative forcing induced by albedo change ...... 27

2.3 Results ...... 29

2.3.1 Surface albedo between four vegetated land covers ...... 29

2.3.2 Shortwave radiative forcing ...... 41

Chapter 3 Shadow price of albedo using DICE/RICE model ...... 45

3.1 Introduction ...... 45

3.2 Data and Method ...... 48

3.3 Global social cost of carbon and albedo based on the DICE model ...... 50

Chapter 4 Summary and Conclusion ...... 55

4.1 Summary ...... 55

4.2 Conclusion ...... 56

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Bibliography ...... 57

Appendix A. Global map supplement ...... 69

List of Tables

Table 2.1 Data sets used to calculate the biophysical forcing of albedo change ...... 21

Table 3.1 Global social cost of carbon and radiative forcing from DICE model for the

year 2020 - 2060 ...... 51

List of Figures

Figure 1.1 Framework of study on biophysical forcing from land use changes ...... 14

Figure 2.1 Schematic of the major climate feedbacks associated with land use changes.

Land-use change by forestry alters the not only carbon sequestration and storage, but

also biophysical forcing, such as from surface albedo change. Our analysis do not

capture all the feedbacks and interactions between land and the atmosphere...... 16

Figure 2.2 Global spatial distributions of four vegetated land covers, including (a)

evergreen needleleaf forest (ENF), (b) deciduous broadleaf forest (DBF), (c)

grasslands (GRA) and (d) croplands (CRO)...... 25

Figure 2.3 Potential land conversions between evergreen needleleaf forest and grasslands

using window searching strategy. Orange pixels were the selected grasslands, and all

the light green grasslands were discarded...... 26

Figure 2.4 Seasonal comparisons of zonally averaged MODIS surface albedo among four

vegetated land covers across the year, including ENF, DBF, GRA and CRO. The

albedo of each vegetation was extracted by MODIS Land Cover product, and

averaged globally for each day...... 30

Figure 2.5 Seasonal comparisons of zonally averaged MODIS surface albedo among four

vegetated land covers across the year, including ENF, DBF, GRA and CRO, in the

Northern Hemisphere and Southern Hemisphere, respectively...... 31

Figure 2.6 Latitudinal comparisons of zonally averaged MODIS surface albedo among

four vegetated land covers, including ENF, DBF, GRA and CRO along the latitude

range of 80° S to 80° N around the day 017 of the year. The zoning averaging was

performed using MODIS Land Cover product...... 33

Figure 2.7 Spatial patterns of MODIS surface albedo around the day 017 of the year

among four vegetated land covers, including ENF, DBF, GRA and CRO. The albedo

of each vegetation was extracted by MODIS Land Cover product...... 36

Figure 2.8 Latitudinal comparisons of zonally averaged MODIS surface albedo among

four vegetated land covers, including ENF, DBF, GRA and CRO along the latitude

range of 80° S to 80° N around the day 233 of the year. The zoning averaging was

performed using MODIS Land Cover product...... 38

Figure 2.9 Spatial patterns of MODIS surface albedo around the day 233 of the year

among four vegetated land covers, including ENF, DBF, GRA and CRO. The albedo

of each vegetation was extracted by MODIS Land Cover product...... 39

Figure 2.10 Spatial patterns of yearly averaged MODIS surface albedo among four

vegetated land covers, including ENF, DBF, GRA and CRO. The albedo of each

vegetation was extracted by MODIS Land Cover product...... 40

xii

Figure 2.11 Latitudinal and seasonal variations in surface albedo over 80° S to 80° N

across the globe for the four major vegetated land covers, including ENF, DBF,

GRA, and CRO. The albedo of each vegetation was extracted by MODIS Land

Cover product...... 41

Figure 2.12 Temporal variation in shortwave radiative forcing (W/m2) at the top of the

atmosphere (TOA) induced by changes in albedo for land conversions from GRA

and CRO to ENF and DBF...... 42

Figure 2.13 Spatial patterns in shortwave radiative forcing (W/m2) at the top of the

atmosphere (TOA) induced by changes in albedo for land conversions from GRA

and CRO to ENF (left column), and GRA and CRO to DBF (right column)...... 43

Figure 3.1 Calculation of global social cost of carbon (SCC) and radiative forcing (RF)

for DICE-2016R with current policy and optimized emissions path for 500 years of

the simulation...... 52

Figure 3.2 Spatial pattern of social cost of radiative forcing with baseline...... 53

Figure 3.3 Spatial pattern of social cost of radiative forcing with optimized path...... 54

Figure A.1 Spatial patterns of global surface albedo...... 69

Figure A.2 Spatial patterns of surface albedo of evergreen needleleaf forest (ENF)...... 70

Figure A.3 Spatial patterns of surface albedo of deciduous broadleaf forest (DBF)...... 70

Figure A.4 Spatial patterns of surface albedo of grasslands (GRA)...... 71

Figure A.5 Spatial patterns of surface albedo of croplands (CRO)...... 71

Figure A.6 TOA shortwave radiative forcing from GRA conversions to ENF...... 72

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Figure A.7 TOA shortwave radiative forcing from CRO conversions to ENF...... 72

Figure A.8 TOA shortwave radiative forcing from GRA conversions to DBF...... 73

Figure A.9 TOA shortwave radiative forcing from CRO conversions to DBF...... 73

Figure A.10 Global land use and land cover of 2001...... 74

Figure A.11 Global land use and land cover of 2012...... 74

Figure A.12 Global land use and land cover changes between 2001 and 2012...... 75

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Chapter 1 Introduction

1.1 Climate-regulation Services

Society and ecosystems are threatened by ongoing global environmental changes, such as global warming, rising sea level, shrinking ice sheets, decreasing snow cover, and more frequent extreme climate events. Most of the changes are attributed at least partially to human activities. Scientific consensus is that the culprit of the current global warming trend is human expansion of the “greenhouse effect” –the warming effect results when the atmosphere absorbs and traps most of the outgoing heat radiating from Earth toward space by greenhouse gas molecules (IPCC, 2015). As a response, efforts have been put into climate adaptation and mitigation for reducing and managing the risks. Integrating ecosystems in combating global warming is an increasingly important option.

Ecosystems are valued for their provisioning, regulating, supporting and cultural services they provide to the society. Maintaining the quality of air and water, providing flood and disease control, erosion prevention and maintenance of soil fertility, or pollinating crops are some of the regulating services provided by ecosystems. For example (FAO, 2018), trees provide shade and forests influence rainfall and water availability both locally and regionally. Wetlands filter effluents, decompose waste through the biological activity of 1

microorganisms, and eliminate harmful pathogens. Vegetation cover prevents soil erosion, which is a key factor in the process of land degradation and desertification, and ensures soil fertility through natural biological processes such as nitrogen fixation. Among these regulating services, climate-regulation service is one of the most important benefits from ecosystems, such as carbon sequestration and storage, local climate regulation, and moderation of extreme events.

Generally, ecosystems regulate climate through multiple pathways, primarily by changing surface energy fluxes, hydrological cycle and atmospheric composition (i.e. greenhouse- gases balance of the atmosphere), which can be classified as either biogeochemical or biophysical pathways (Anderson-Teixeira et al., 2012, Bonan, 2008, Chapin et al., 2008).

The biogeochemical pathway has been acknowledged by policies and strategies for climate mitigation, which is achieved through the control on greenhouse-gas fluxes. Biophysical factors consist of reflectivity, evaporation, surface roughness, canopy conductance, etc., all of which affect temperature, and have rarely been taken into consideration into climate polices. Currently, climate mitigation through land management account for only biogeochemical pathways. However, land alterations by forestry and other activities modify not only carbon stocks, but also energy partitioning, water cycling, and atmospheric composition (Zhao and Jackson, 2014). In many cases, biophysical factors can alter temperature even more strongly than does the greenhouse-gas regulation, often in a conflicting way (Jackson et al., 2008). Therefore, studying biophysical interactions with

the atmosphere of forestry activities and land-use changes can avoid wasteing millions of dollars in some climate mitigation projects that provide little climate benefits or are counter-productive (Jackson et al., 2008).

1.2 Carbon Sequestration and Storage

Since the times of the industrial revolution, the amount of carbon dioxide (CO2) and other greenhouse gases (GHGs) found in the atmosphere has increased significantly. CO2 is an important heat-trapping gas and the largest contributor to the greenhouse effect. It is released through human activities such as deforestation, land-use changes and burning fossil fuels like coal and oil, as well as natural processes such as respiration, wildfires and volcanic eruptions (IPCC, 2015). A variety of practices has been carried out to remove

GHGs, especially CO2, from the atmosphere to alleviate the effect of global warming. One natural and effective practice is climate mitigation and forest management, which regulate the global climate by storing GHGs. As trees and plants grow, they remove CO2 from the atmosphere and incorporate it into their tissues. This long-term process involved in carbon capture and depositing it in a reservoir is called carbon sequestration and storage.

Historically, land-use change and soil cultivation have been important sources of GHGs to the atmosphere. Land-use change directly affects the exchange of GHGs between terrestrial ecosystems and the atmosphere, and has been a major driver of global warming (Solomon,

2007). Since 1850, land-use change has released ~150 billion metric tons of carbon, accounting for 35% of anthropogenic CO2 emissions (Houghton, 2003, Zhao and Jackson, 3

2014). According to a research on GHG emission in 2005 (Herzog, 2009), land-use change accounts for 18.2% of the global GHG emissions, including deforestation, afforestation, reforestation and harvest/management. Agriculture has contributed 13.5% of the total global GHG emissions. Between 1990 and 2010, land-use change and land management have contributed about 1.45 Pg of carbon to the total carbon released in China (Lai et al.,

2016). From 1990 to 2007, gross CO2 emission from tropical deforestation were equal to

40% of global fossil fuel emissions (Anderson-Teixeira et al., 2012, Pan et al., 2011).

Terrestrial ecosystems remove nearly 3 billion tons of anthropogenic carbon every year

(Canadell and Raupach, 2008). Forests have an important role in the global carbon cycle and are valued for the services and benefits they provide to human beings, society, economics and natural creatures, especially regarding to climate change. Specifically, forests provide habitats for biodiversity conservation, manage clean water supplies and offer resilience against disasters, sustain livelihood and economic opportunities, and are the greatest carbon pools (Keenan et al., 2015, MacDicken et al., 2016). They absorb and store carbon in both above and below ground biomass, and continuously exchange CO2 with the atmosphere, due to both natural processes and human forestry activities (Nam et al., 2015). They are important sinks of carbon dioxide, and store about 45% of terrestrial carbon, contribute 50% of terrestrial net primary production, and can sequester large amounts of carbon annually (Bonan, 2008). According to USDA (United States

Department of Agriculture), U.S. forests currently serve as a carbon 'sink', offsetting

approximately 12 to 19% of U.S. emissions from burning fossil fuels, and from 10 to 20% of U.S. emissions each year (Ryan et al., 2010). However, the extent of the world’s forests continues to decline as human populations continue to grow and demand for food and land increases (IPCC, 2015). Forests cover 3999 million ha in tropical, temperate, and boreal lands, 30.6% of the global land surface, decreased from 31.6% in 1990 (Keenan et al., 2015,

MacDicken et al., 2016).

1.3 Surface Energy Fluxes

1.3.1 Albedo and global warming

Forest carbon storage has been the primary way forests have been tied to climate change mitigation efforts to reduce net CO2 emissions to the atmosphere and so limit the radiative forcing of climate change (Betts, 2000, IPCC, 2015, Lutz and Howarth, 2014). However, the overall impact of forests on climate also depends on other properties associated with biophysical forcing, such as reflectivity, or albedo. Land surface albedo plays an essential role in regulating the global radiation balance and influencing surface heat flux exchange

(Yu et al., 2017). It measures how much of the Sun’s energy is reflected back into space.

Overall, the albedo of the Earth has a cooling effect. Albedo is defined as the ratio of the light flux in solar wavelengths reflected from a surface area into a hemisphere to the total incoming incident flux (Gao et al., 2005). Thus the albedo of a surface is measured on a scale from 0 to 1, where an albedo of one would apply to a perfectly white diffuse reflector, and an albedo of zero would apply to a perfectly black body (Schneider and Hare, 1996).

For instance, snow has a rather high albedo value, ranging from as high as 0.7-0.9 for fresh snow to 0.5-0.6 for old snow or sea ice, whereas water has a low albedo between 0.02-0.1

(Schneider and Hare, 1996).

Albedo is a critical parameter affecting the earth’s climate by determining how much radiation a surface absorbs and is required by global and regional climatic modeling and surface energy balance monitoring (Liang et al., 1999). In a cloudless environment with high levels of solar radiation, albedo becomes an important factor in surface heating.

Albedo of a forested landscape is generally lower and darken the land surface compared to a cultivated land, such as pasture, cropland, barren or sparsely vegetated area. When snow is lying, the decreasing albedo exerts a positive radiative forcing on climate, consequently dampening the cooling effect of carbon sequestration of forests (Betts, 2000, Zhao and

Jackson, 2014). According to some research results (Barry and Chorley, 2009, Buchdahl,

1995), the albedo of deciduous forests is about 0.15 to 0.18 whilst that of coniferous forests is 0.09 to 0.15. Tropical rainforest reflects even less energy, approximately 7% to 15% of that which it receives (Buchdahl, 1995). In comparison, the albedo of a desert sand is about

0.4 (Tetzlaff, 1983).

The presence of the continental forests affect the global radiation balance and potentially absorb more heat. For instance, Jackson and others (Jackson et al., 2008) examined monthly shortwave surface albedo for dominant US land cover types in the northwest,

northeast, southwest, and southeast based on satellite observations. According to their results, albedo in croplands is substantially higher than in nearby forests in all regions.

They also found that in the northeast and southeast, deciduous broadleaf forests have higher albedo values than evergreen needleleaf forests during summer.

As to a certain land use and land cover type, a variety of factors, such as vegetation type, normalized difference vegetation index (NDVI), latitude and snow cover, would affect the surface albedo. In boreal areas, a layer of snowfall would increase surface albedo, reflecting away sunlight and leading to local cooling. However, the snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming (Déry and Brown, 2007, Yu et al., 2017). Yu and others (Yu et al., 2017) selected Heilongjiang Basin, where the snow cover has changed, as the study area and used the Weather Research and Forecasting (WRF) model to simulate the influences of snow cover changes on the surface radiation budget and heat balance.

They found that compared with typical land cover changes, the influence of snow cover changes on net radiation and sensible heat flux were 60% higher than that of land cover changes.

In addition to snow, cloud albedo also has substantial influence over global temperatures.

Clouds reflect sunlight even more than land and water, contributing to the cooling effect of albedo. Though their albedo values vary based on their own properties, theoretically

ranging from a minimum of near 0 to a maximum approaching 0.8 (Wikipedia). Albedo also changes due to seasons and human activities. Zhao and Jackson (2014) assessed the climatic impact of vegetation replacement across North America by comparing satellite- derived albedo between adjacent vegetation types. They found that albedos of grassland and evergreen needleleaf forest differed by 0.21 in January and 0.054 in July, and a larger net cooling is expected for planting deciduous broadleaf forest than evergreen needleleaf forest.

As a critical property of the Earth’s surface, surface albedo affects the global climate by regulating the radiation balance and influencing surface heat flux exchange. Appropriate quantification and valuation of the climatic-economic benefits of biophysical forcing induced by albedo change is important in the context of combating global warming, and to some extent reduce bias in the estimation of carbon-centric climatic impacts of forests. As for quantification, relevant research has advanced in the past several years, though studies vary with respect to the specific methodology and datasets. In general, studies tend to include albedo in forest management efforts by converting albedo to a quantity of forest carbon stock with the same equivalent radiative forcing (Lutz and Howarth, 2014). A common method was employed to link albedo to forest climate policies. The method calculates radiative forcing induced by albedo change, then converts the radiative forcing into carbon dioxide equivalents (CO2-e). When it comes to the valuation of the climatic benefits of albedo, adequate research is lacking. Sjølie and others (Sjølie et al., 2013) used

an economic model to simulate GHG fluxes and albedo impacts for the next decades.

Albedo is incorporated in a carbon tax/subsidy scheme with self-selected shadow prices

(i.e., the estimated price of a service, such as albedo).

1.3.2 Land surface temperature

Ecosystems, particularly forests can regulate local and regional temperature through both biogeochemical and biophysical processes, and play a critical role in cooling the surface in almost all regions of the Earth. Biophysical processes are complicated and can result in either warming or cooling (Li et al., 2015). Thus, the overall climate regulation effect of forests does not necessarily alleviate the global warming and have a cooling effect. It depends largely on the synergy and tradeoffs of carbon sequestration, surface energy fluxes and evapotranspiration. Forestry activities and land use changes, such as deforestation, reforestation and afforestation, not only change the ability of carbon uptake, but also alter biophysical processes, including reduction or increasing in evapotranspiration, in surface albedo, and in surface roughness and hence turbulent exchange (Zhang et al., 2014).

A variety of studies have been carried out to determine the cooling and warming effects of forests on local and regional scale based on both climate models and satellite observations.

Climate models simulating continental scale deforestation suggest a warming effect of land clearing on the surface air temperature in the tropical zone and a cooling effect in the boreal zone due to different control of biogeochemical and biophysical processes (Zhang et al.,

2014). For example, by employing a realistic coupled atmosphere-biosphere model, the 9

research in Amazonia found that forest replacement by a degraded pasture in model simulations would result in a surface temperature increasing by 1°-3°C (Nobre et al., 1991,

Shukla et al., 1990). A tropical deforestation experiment (Henderson‐Sellers et al., 1993) throughout the Amazon Basin and Southeast Asia had a similar result as a smaller temperature increase. As a comparison, results (Bonan et al., 1992) from a global climate model showed that the boreal forest warms both winter and summer air temperatures, relative to simulations in which the forest is replaced with bare ground or tundra vegetation.

Another model simulation, which was performed by a three-dimensional coupled global carbon-cycle and climate model, showed global-scale deforestation has a net cooling influence on the climate, because the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration

(Bala et al., 2007).

However, temperature patterns generated by climate model simulations from deforestation may not be consistent with the true weather observations on local scale, and not able to accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties (Li et al., 2015). Therefore, some researchers used data from other resources such as satellite imagery and weather stations instead of climate models. Li and others (Li et al., 2015) found that tropical and temperate forests have a cooling effect, and boreal forests have a net warming effect annually based on satellite observations. Zhang and others (Zhang et al., 2014) found a transitional latitude of about 35.5°N that demarks

deforestation warming to the south and cooling to the north. In general, the observed temperature change from forestry activities, such as deforestation and afforestation, are consistent with climate model predictions, which is dependent on the latitude.

1.4 Hydrological Cycle

The increasing concentration of CO2 and other green-house gases in the atmosphere is expected to cause global mean temperature to rise (IPCC, 2015). The likelihood of such an increase in future resulting from changes in atmospheric composition is already established with a fair degree of confidence (Chattopadhyay and Hulme, 1997, Houghton,

1996). However, changes in other biophysical climatic elements such as surface roughness, reflectivity, precipitation, cloudiness, humidity and windiness, and derived quantities such as soil moisture and evaporation also have a significant impact on global mean temperature, and more importantly are likely to follow changes in temperature (Chattopadhyay and

Hulme, 1997, McKenney and Rosenberg, 1993). Moreover, these changes are complicated and to specify on regional and global scale are difficult to establish directly through observations, especially associated with evapotranspiration.

Evapotranspiration (ET) is a compound term describing the physical process of water transfer into the atmosphere by evaporation from soil and transpiration through vegetation

(McKenney and Rosenberg, 1993). It is an important component of the hydrological cycle and regulates latent heat flux. In tropics, forests cool regionally by increasing the evaporation of water from land to air, helping to form clouds that contributing to additional 11

cooling by reflecting sunlight back to space (Jackson et al., 2008). Nevertheless, they also contribute to warming at the same time, because of the condensed water vapor retains heat

(Anderson et al., 2011). At the regional scale, the climatic effect of evapotranspiration is complex and difficult to specify and quantify. Climate model simulations show that tropical forests maintain high rates of evapotranspiration, contributing to cooling effects, which means the surface warming arising from the low albedo of forests is offset by strong evaporative cooling (Bonan, 2008). This cooling effect has been proved by several studies in Amazonia, tropical Africa and Asia. However, boreal forests have much slower rates of carbon sequestration and negligible evapotranspiration. Compared with snow and ice, boreal forests are darker and absorb much more sunlight. As a result, forests in boreal areas can warm surface locally and regionally and deforestation has a cooling effect. As for deforestation in temperate areas, the local cooling and warming effects remain uncertain.

It is dependent on evapotranspiration, increased surface roughness and albedo. Some studies suggest that deforestation (replacing forests with grasslands or croplands) in temperate regions cools surface air temperatures (Bala et al., 2007, Diffenbaugh and Sloan,

2002, Oleson et al., 2004). Other studies present the opposite results (Defries et al., 2002,

Jackson et al., 2005, Juang et al., 2007).

1.5 Objectives and Framework

The overall goal of this work is to quantify and valuate the biophysical forcing and climatic impacts of land use and land cover changes across the globe through the combined use of remotely sensed observations and climate model outputs. Specifically, two objectives were 12

to (1) identify and quantify the climatic impacts of forests from land use changes, and (2) estimate the shadow price of carbon and albedo globally and regionally. The first objective leverages the availability of satellite-based global surface albedo measurements and land cover products to accurately quantify how land conversions from one vegetation to another will alter the portion of sunlight being reflected off (i.e., albedo) and how such albedo changes will affect climate and temperature in terms of radiative forcing. The second objective goes beyond the quantification of radiative forcing to put a price on the albedo- induced biophysical forcing effects. This offers a rigorous way to value biophysical climate regulation services of ecosystem, which is an area still largely unexplored in the literature but critical for informing sound climate mitigation policies. Figure 1.1 presents an overview of specific tasks and the theoretical framework.

Quantifying and Valuating Radiative Forcing of Land-use Changes from Forestry Activities Radiative forcing of land-use changes Land use management and climate Shadow price of albedo using DICE/ mitigation strategy RICE

Biophysical Properties Surface Canopy Other Reflectivity roughness conductance properties Albedo Satellite imagery MODIS Change from Land-use changes from potential/historical forestry activities Forestry

Evergreen Needleaf Cropland Activities

Deciduous Broadleaf Grassland

Albedo_new Albedo_old CERES Shortwave Albedo change RF change Radiative Forcing Induced Surface Albedo Radiative Forcing by Albedo White-sky Black-sky Surface albedo change albedo albedo Change

Diffuse Direct Reflectance Transmittance radiation radiation

Shadow Albedo change from land use changes Price of Albedo Δ Radiative forcing DICE/RICE Social Welfare and Consumption Based on

Carbon emissions DICE Model

Figure 1.1 Framework of study on biophysical forcing from land use changes 14

Chapter 2 Biophysical forcing from potential forestry activities

2.1 Introduction

Climate change poses a fundamental threat to ecosystems, biodiversity and human society over the decades and centuries to come. At the same time, the attention paid to sustainable forest management with a climate change focus is high (MacDicken et al., 2016). Generally, strategies to combat and mitigate climate change recognize that carbon sequestration in the terrestrial ecosystems can reduce the build-up of carbon dioxide in the atmosphere

(Marland et al., 2003), and the climate benefits of reducing forest loss and improving forest management are widely accepted (Edwards et al., 2010). More land is designated as permanent forest, and more assessment, monitoring, reporting and planning is taking place

(MacDicken et al., 2016). However, the enhancement of carbon sequestration through forest planting has raised concern that these forestry activities may have potential negative consequences for climate and further accelerate global warming. Thus, understanding of the climate response from forestry activities and their biogeochemical and biophysical mechanisms (Figure 2.1) are undoubtedly important in formulating policies to optimize climate benefits of forestry or land management activities (West et al., 2011, Zhao and

Jackson, 2014).

Carbon Sequestration and Storage

Ecosystems to combate climate change Biogeochemical Biophysical mechanism mechanism Climate-regulation policies and forest managements

Local and regional cooling and warming effects Tropical Open land Temperate Forest Boreal

Biophysical Mechanism

Albedo Canopy conductance Surface roughness Other properties

Shortwave Land surface ET/latent heat Sensible heat forcing temperature

Biophysical forcings of land-use change

Albedo change from forestry activities

DICE/RICE Radiative Forcing

Shadow Price of Albedo Surface Energy Hydrological Fluxes Cycle Forestry Climate activities change

Climate Change

Figure 2.1 Schematic of the major climate feedbacks associated with land use changes. Land-use change by forestry alters the not only carbon sequestration and storage, but also biophysical forcing, such as from surface albedo change. Our analysis do not capture all the feedbacks and interactions between land and the atmosphere.

Land use changes resulting from anthropogenic activities such as deforestation, afforestation, reforestation and forest management, not only change the carbon sequestration and storage, but also modify biophysical factors, such as albedo, surface roughness, sensible and latent heat fluxes, canopy conductance, soil moisture, surface temperature, emissivity, leaf area and rooting depth (Anderson et al., 2011, Jayawickreme et al., 2011, Kueppers et al., 2007, Zhao and Jackson, 2014). Changes of biophysical factors will lead to changes in energy partitioning, water cycling, and atmospheric composition

(Zhao and Jackson, 2014), which can alter temperatures much more than carbon sequestration does (Jackson et al., 2008), resulting in dampening or amplifying climate change. To examine the changes in climate benefits from land use changes, a variety of studies have used climate models and satellite observations. Juang and others (Juang et al.,

2007) showed that, in the southeastern United States, changes in albedo alone can warm the surface by 0.9°C for the grass-covered land to planted pine forest conversion, and 0.7°C for the grass-covered land to hardwood forest conversion on annual time scales.

The biogeochemical mechanism of forests regarding carbon sequestration and storage has been well-established, whereas the biophysical processes are more complex and requires more inputs to achieve a better understanding of the full climate system. Taking evapotranspiration as an example, it has a strong or weak cooling effect based on where this process happens, contributing to a high concentration of water vapor in the atmosphere that traps heat. At the same time, strong evapotranspiration effect means large latent heat

fluxes that result in the development of clouds, which reflect solar radiation back to space

(Anderson et al., 2011). Meanwhile this process also contributes to warming, because clouds consist of condensed water vapor that retains heat. These complexities raise questions as to how to incorporate and integrate biophysical processes with biogeochemical mechanism into climate mitigation strategies.

Typically radiative forcing (RF) is used to assess the quantitative comparisons of carbon sequestration and biophysics associated with climate regulation (Zhao and Jackson, 2014), which is defined as the perturbations to the balance between radiation absorbed and emitted by the Earth caused by natural and anthropogenic drivers (Betts, 2000, Rotenberg and

Yakir, 2010). It measures the capacity to affect the energy balance, thereby contributing to climate change, and is expressed in Watts per square meter (W/m2). Positive radiative forcing, either longwave or shortwave, results in an increase in global energy budget and ultimately leads to warming, whereas negative radiative forcing results in a decrease in the energy budget and thereby leads to cooling (Gray, 2015, IPCC, 2015). According to the

Intergovernmental Panel on Climate Change (IPCC, 2015), the total anthropogenic radiative forcing over 1750 – 2011 was calculated to have a warming effect of 2.3 W/m2, and it has increased more rapidly since 1970 than during prior decades.

Recognition of biophysical climate regulation of ecosystems has grown. In boreal areas, land alteration associated with deforestation will increase surface albedo and consequently

reduce the radiative forcing regionally and locally, which reduces mean temperature, even though deforestation could weaken the evapotranspiration effect and reduce the surface roughness (Zhang et al., 2014). In tropical areas, deforestation tends to warm the surface because of the reduction of the dominating cooling effect from evapotranspiration. In temperate areas, the overall impact of deforestation on the mean temperature is determined by the balance of both biogeochemical and biophysical processes. Therefore, given the goal of mitigating climate change, considerations of biophysical factors and their effects on the surface albedo, the fluxes of sensible and latent heat to the atmosphere, and the distribution of energy within the climate system (Marland et al., 2003) also are supposed to be incorporated into climate mitigation strategies and policies.

Despite growing recognition of the biophysical regulation of climate by ecosystems, quantifying their effects is still a challenge. In addition, existing efforts at large scale are lacking. Quantify biophysical regulations, typically considered albedo and converted it to carbon dioxide equivalent with the same equivalent radiative forcing, and economic valuation of biophysical forcing. Thus this study is trying to fill this gap by improving the assessments and subsequently estimating the shadow price of the ecosystem regulation service. The global assessment of albedo-induced radiative forcing is just the first step toward realistic assessment of economic costs.

This work focused on the biophysical forcing of albedo changes from potential land conversions between forests and non-forests. The aim of this study was to examine the biophysical forcing and climatic impacts of potential land use and land cover changes across the globe based on remotely sensed observations and climate model outputs. In this study, four different vegetation types, evergreen needleleaf, deciduous broadleaf, cropland and grassland were identified and compared. Specifically the objectives were to (1) evaluate the differences in surface albedo between contrasting vegetation temporally and spatially, and (2) calculate radiative forcing changes due to the albedo change from potential land replacement.

2.2 Data and Method

2.2.1 Data

This study focused on four types of vegetation across the globe, including evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), grasslands (GRA) and croplands (CRO) associated with surface albedo and shortwave radiative forcing. To analyze the temporal and spatial variability of albedo of adjacent vegetation types and land replacement, a variety of datasets were used (Table 2.1), including Global Land Cover

Facility (GLCF), Moderate Resolution Imaging Spectroradiometer (MODIS), The Clouds and the Earth's Radiant Energy System (CERES), Coupled Model Intercomparison Project

5 (CMIP5). The GLASS albedo product retrieved from GLCF has a temporal resolution of

8 days, and was derived from MODIS data with a spatial resolution of 1km in tile-based

sinusoidal projection, comprised of black-sky and white-sky albedo. As for land use and land cover data, the MODIS yearly land cover type product (MCD12Q1) was used. It contained five classification schemes with a spatial resolution of 500 m (Friedl et al., 2010).

The CERES data included monthly averaged products (Energy Balanced and Filled, EBAF) of top of atmosphere (TOA) and surface longwave and shortwave fluxes, with a spatial resolution of 1°×1° (Wielicki et al., 1998). In addition, the CMIP5 multi-model outputs were used for the ratio of the direct to diffusive shortwave radiation to calculate the actual

(blue-sky) albedo, which was a combination of black-sky and white-sky albedo.

Table 2.1 Data sets used to calculate the biophysical forcing of albedo change

Data Source Product Years Resolution Reference

(Liang and Liu, 2012, Liu et Albedo GLCF GLASS 2000-2012 1km×1km al., 2013a, Liu et al., 2013b, Qu et al., 2013)

Land cover MODIS MCD12Q1 2001-2012 5'×5' (Friedl et al., 2010)

TOA fluxes CERES EBAF-TOA 2000-2017 1°×1° (Loeb, 2017b)

Surface CERES EBAF-Surface 2000-2016 1°×1° (Loeb, 2017a) fluxes

Diffuse CMIP5 ACCESS1.0 1850-2005 1.25°×1.875° (Taylor et al., 2012) radiation

Direct CMIP5 HadGEM2-AO 1860-2005 1.25°×1.875° (Taylor et al., 2012) radiation

2.2.2 Comparisons of surface albedo between contrasting vegetation

Both GLASS Albedo product and MODIS Land Cover product were used to evaluate differences in surface albedo between contrasting vegetation types. The evaluation emphasized paired adjacent sites (i.e., pixels) for two contrasting vegetation types. These adjacent sites were found in ecotones and disturbed lands where the potential of future land-cover changes due to either natural processes or human activities was high (Zhao and

Jackson, 2014). In this study, the adjacent land types were assumed to share the same background state defined by the incoming solar radiation, the incoming longwave radiation and air temperature at the blending height above the ground. Therefore, vegetation controls could be isolated on surface biophysics to the greatest extent possible and to minimize the influences of confounding factors such as topography, solar angle, and rainfall.

GLASS albedo product was averaged across years from 2001 to 2012 for each of the 46 eight-day observation periods to smooth out inter-annual variability because only surface albedo differences for vegetation types were compared in this study (Appendix: Figure

A.1). Then, to determine the spatial distributions of vegetation, a land use and land cover map at 500-m resolution was generated by synthesizing the twelve yearly 500-m MODIS land cover layers for 2001-2012 (Appendix: Figure A.10-11). A pixel was assigned a particular vegetation class only if the pixel was classified as this class for more than six out of twelve years, otherwise, the pixel was discarded from this analysis. This filtering helped to suppress the confounding effects of classification errors and potential land cover changes

that occurred between 2001 and 2012 (Appendix: Figure A.12). In addition, the resultant

500-m land cover map was aggregated to 1-km resolution to match the resolution of

GLASS albedo product, and therefore, the GLASS albedo was extracted for the vegetation types across the globe.

The GLASS albedo product (derived from MODIS shortwave albedo product) comprises black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bi- hemispherical reflectance), but no consensus on the choice of albedo was in the literature

(Li et al., 2015). Black-sky albedo is also called direct beam albedo and white-sky albedo is completely diffuse albedo representing bi-hemispherical reflectance under isotropic skylight illumination (Gao et al., 2005, Schaaf et al., 2002, Zhao and Jackson, 2014). For instance, Luyssaert and others (Luyssaert et al., 2014) used black-sky albedo to calculate the potential cooling or warming induced by albedo changes from land use change, while

Peng and others (Peng et al., 2014) used white-sky albedo. Some other studies combined black-sky and white-sky albedo, i.e. actual (blue-sky) albedo, through the ratio of the direct to diffusive shortwave radiation. For example, Li and others (Li et al., 2015) used the average of black-sky and white-sky albedo as the actual albedo because the difference between these two albedos is very small and they are highly correlated (R=0.9992). In addition, Zhao and Jackson (Zhao and Jackson, 2014) used both white-sky and actual albedo, but they employed a more robust method to calculate the actual surface albedo, which was estimated as the average of black-sky (direct) and white-sky (diffuse) albedos

weighted by direct and diffuse downward fluxes from the regional climate simulation of

WRF-CLM (Weather Research Forecast-Community Land Model).

In this study, the actual surface (blue-sky) albedo was calculated (Equation 2.1) and used for each four vegetation types (i.e., evergreen needleleaf forest, deciduous broadleaf forest, grasslands and croplands).

I   dir WSA BSA ID    dif WSA BSA (2.1) sfc I 1 dir 1 D Idif

Where sfc , WSA and BSA are the actual surface (blue-sky) albedo, white-sky albedo and

black-sky albedo, respectively. Idir and Idif are direct and diffusive shortwave radiation, respectively, while D presents the ratio of the direct to diffusive shortwave radiation.

2.2.3 Potential land conversions between vegetation

To better understand the net climate effect of land use and land cover changes on global climate, transitions from non-forest to forest vegetation was emphasized relevant to climate mitigation policies, specifically grassland (GRA) and cropland (CRO) conversions to evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) (Figure 2.2). A window searching strategy was applied to find all available adjacent sites to compare surface albedo between contrasting vegetation types. This strategy ensured that all vegetation types and their contrasting vegetation types are close in distance and share

similar background. Additionally, this circular window of 30 km in diameter was moved across the study area to identify all the possible pairs of adjacent sites of contrasting vegetation. After all the possible pairs of adjacent sites were selected, the grassland and cropland were converted into evergreen needleleaf forest and deciduous broadleaf forest, replacing their original surface albedo value for each pixel with the averaged forest albedo value within the circular window of 30 km in diameter of that particular pixel.

Figure 2.2 Global spatial distributions of four vegetated land covers, including (a) evergreen needleleaf forest (ENF), (b) deciduous broadleaf forest (DBF), (c) grasslands (GRA) and (d) croplands (CRO).

Figure 2.3 Potential land conversions between evergreen needleleaf forest and grasslands using window searching strategy. Orange pixels were the selected grasslands, and all the light green grasslands were discarded. 26

Specifically, using land replacement between ENF and GRA to illustrate this procedure

(Figure 2.3), for each ENF pixel (i.e., dark green pixel), all the GRA pixels (i.e., orange pixels) within a 15-km radius of it were identified, and other pixels (light green) would be discarded. Then each selected GRA pixel (i.e., orange pixel) was converted to ENF with the averaged actual albedo values of ENF within 15-km radius of GRA (i.e., GRA conversions to ENF).

2.2.4 Shortwave radiative forcing induced by albedo change

This study used a method similar to Zhao and Jackson (2014) to evaluate the shortwave radiative forcing induced by altered surface albedo from land cover change at three levels: surface, atmosphere, and top of atmosphere (TOA). Specifically, radiative forcing at the

surface ( SFsfc ) affects the surface energy balance and partitioning. Radiative forcing at the

TOA ( SFtoa ) is the quantity related to the change in global mean temperature through

climate sensitivity parameters. Atmospheric radiative forcing ( SFatm ) is the difference between TOA and surface radiative forcing (Equation 2.8). Calculation of TOA radiative

forcing ( ) require translating surface albedo (sfc ), as measured by GLASS albedo

using Equation 2.1, to planetary albedo at the TOA (toa ). Here we used a simple yet effective scheme (Equation 2.2) offered by the single-layer radiative transfer model of Liou

(Liou, 2002) to convert surface albedo to TOA albedo .

 2 Ftoa T toa()  sfc R   sfc (2.2) SR1sfc 27

 Where S and Ftoa are the incident and reflected solar fluxes at the TOA, respectively; R and T are the single-pass atmospheric reflectance and transmittance, respectively. The reflectance and transmittance were estimated following the method of Donohoe

(Donohoe and Battisti, 2011) for Equation 2.2 using the CERES TOA and surface shortwave fluxes. The method is given by Equation 2.3-2.5.

 2 2 2 3 2 2 Ftoa  S[] R sfcT   sfc RT   sfc R T  2 22T (2.3) SR  SsfcTRR[1  (  sfc )  (  sfc ) ]  SR S  sfc 1sfc R

 T FSSURF  (2.4) 1sfc R

T FSURF  sfcFF sfc SURF (2.5) 1sfc R

  Where Ftoa is the upward shortwave fluxes at the TOA and FSURF is the downward shortwave fluxes at the surface, respectively. Our estimated atmospheric reflectance and transmittance characterize the actual atmospheric optical properties and allow us to compute surface, TOA, and atmospheric shortwave radiative forcing as follows (Equation

2.6-2.8):

SFtoa  S [toa (  sf c ,2 )  t oa (  sfc,1 )] (2.6)

 SFsfcFF SURF( sfc,2 )  (1   sfc,2 )  SURF (  sfc,1 )  (1   sfc,1 ) (2.7)

SFatm SF toa SF sfc (2.8) 28

Here, the surface, TOA, and atmospheric shortwave radiative forcing are driven by a

change in surface albedo from sfc,1 to sfc,2 while assuming that the atmospheric optical properties, including R and T , remain unaffected. A positive shortwave radiative forcing in Equation 2.6-2.8 indicates that the system absorbs extra solar radiation after land conversion. Using Equation 2.1-2.8, the 46 eight-day observation periods radiative forcing were calculated for four scenarios of non-forest to forest conversions at a 1km×1km resolution, compatible with the spatial and temporal resolutions of and derived from the CERES data.

2.3 Results

2.3.1 Surface albedo between four vegetated land covers

Albedo depends on vegetation and soil properties to a large extend. Lands covered with woody vegetation usually have a lower albedo value compared with lands with short vegetation or herbaceous plants. Figure 2.4 shows albedo values in open land (GRA and

CRO) substantially higher than in nearby forests (ENF and DBF) almost throughout the whole year. The averaged surface albedo of ENF, DBF, GRA, and CRO are 0.16, 0.14,

0.25, and 0.20, respectively, which means GRA and CRO reflect more sunlight than ENF and DBF, with relatively more cooling surface air temperatures. In contrast, forests (ENF and DBF) have more evapotranspiration, and transmit more heat to the atmosphere as the form of latent and sensible heat, respectively, cooling surface air temperature locally compared to grasslands or croplands. However, only the climatic effect of albedo was

emphasized, and other biophysical factors and properties, such as longwave and/or shortwave radiative forcing induced by changes in evapotranspiration, land surface temperature and atmospheric conditions, would not be taken into consideration in this study.

Figure 2.4 Seasonal comparisons of zonally averaged MODIS surface albedo among four vegetated land covers across the year, including ENF, DBF, GRA and CRO. The albedo of each vegetation was extracted by MODIS Land Cover product, and averaged globally for each day.

Figure 2.5 Seasonal comparisons of zonally averaged MODIS surface albedo among four vegetated land covers across the year, including ENF, DBF, GRA and CRO, in the Northern Hemisphere and Southern Hemisphere, respectively. 31

As we can see from Figure 2.4 and Figure 2.5, among all vegetation types, actual surface albedo in both spring and winter was substantially higher than in summer and autumn, implying the critical role of snow in determining surface albedo. Deciduous broadleaf forests are dominated by trees that lose their leaves each year, and mainly distributed in temperate regions with warmer continental and humid subtropical climates. Therefore,

DBF has a rather stable surface albedo value throughout the year compared with other three vegetation. In contrast, ENF, GRA and CRO have a significant fluctuation and variation of surface albedo across the year, especially between winter-time and summer-time. This is mostly because of the existence of snow, which is presented by the albedo variation associated with the day of year. For instance, in summer time ENF has the lowest albedo and absorbs more heat than others, but during winter time, it has a much higher albedo value than DBF does, even though it still has a lower albedo value than GRA and CRO. In general, GRA has the higher globally averaged albedo value all year around than other vegetation.

Actual surface albedo values change not only associated with day of year, but also depending on spatial variation and/or latitude. Taking day of year 17 as an example of winter period (Figure 2.6), the zonally averaged MODIS actual surface albedo over 80° S to 80° N was 0.23 ± 0.05, 0.16 ± 0.05, 0.36 ± 0.13, and 0.29 ± 0.11 (mean ± SD) for ENF,

DBF, GRA, and CRO, respectively. In boreal regions (i.e., high latitude areas), especially where snow is present, surface albedo value of all vegetated land cover types is

substantially higher than in other regions (i.e., low latitude areas), again implying the critical role of snow in determining surface albedo.

Figure 2.6 Latitudinal comparisons of zonally averaged MODIS surface albedo among four vegetated land covers, including ENF, DBF, GRA and CRO along the latitude range of 80° S to 80° N around the day 017 of the year. The zoning averaging was performed using MODIS Land Cover product.

Unlike multilayered forest canopies, GRA and CRO, which are covered with little or no low-lying live or dead biomass in winter, are more likely to be buried under snow. As a consequence, surface albedo in GRA and CRO has a more noticeable difference between winter and summer time compared with in ENF and DBF.

In general, all vegetation lands have a consistent surface albedo in mid-latitude and low- latitude, with ENF having the lowest albedo value and CRO having the highest albedo value. However, as we can see from Figure 2.6, there is a bump of surface albedo in GRA around the latitude range of 20° N to 35° N, indicating abnormally higher surface albedo values in that grassland than in other areas.

With reference to the map at the lower left of Figure 2.7 (and Appendix: Figure A.10-11), we can see an ecotone in Africa around the latitude range of 20° N to 35° N, and it’s classified as grasslands according to the MODIS land cover product (MCD12Q1). An ecotone is a region of transition between two biomes. Because of the distinguishing features of the ecotone, such as a sharp vegetation transition, surface albedo in GRA has an abrupt change near that region. To be more specific, that ecotone is the Sahel (Appendix:

Figure A.10-11), an ecoclimatic and biogeographic zone of transition in Africa between the Sahara to the north and the Sudanian Savanna to the south. It is mostly covered in grassland and savanna, with areas of woodland and shrubland, and has a tropical, hot steppe climate, which is typically hot, dry and somewhat windy all year along. Also, it has a high to very high sunshine duration year-round. Consequently, the surface albedo is much higher than in other regions.

In addition, even though the surface albedo in DBF at mid-latitudes and low-latitudes is higher than in ENF, the albedo value is much higher in ENF than in DBF at high-latitudes

and ENF has a dominating distributed area in boreal regions, leading to a higher globally averaged albedo value of ENF than DBF.

The standard deviation of the globally averaged MODIS actual surface albedo over 80° S to 80° N was 0.05, 0.05, 0.13, and 0.11 for ENF, DBF, GRA, and CRO, respectively, which means the surface albedo values of GRA and CRO are more spread out than of ENF and

DBF. Also, this is supported by the spatial patterns of MODIS surface albedo around the day 017 of the year among four vegetated land covers (Figure 2.7).

The upper left of Figure 2.7 is the spatial pattern of surface albedo of ENF, which shows that ENF has similar surface albedo values across the globe, and most of the evergreen forests are found throughout the high northern latitudes, between the tundra and the temperate forest. For example, in North America it covers most of inland Canada as well as parts of the extreme northern continental United States, where it is known as the

Northwoods. In Eurasia, it covers northern countries, such as Finland, Sweden, Norway and much of Russia. The upper right of Figure 2.7 is the spatial pattern of surface albedo of DBF, and most of them are found in North and South America, sharing similar surface albedo values. The lower right of Figure 2.7 is the spatial pattern of surface albedo of CRO, and they are distributed all over the world. Around the day 017 of the year, croplands in northern areas have a higher albedo value because of the presence of snow.

Figure 2.7 Spatial patterns of MODIS surface albedo around the day 017 of the year among four vegetated land covers, including ENF, DBF, GRA and CRO. The albedo of each vegetation was extracted by MODIS Land Cover product.

The lower left of Figure 2.7 is the spatial pattern of surface albedo of GRA, from which a considerable regional variation can be observed. Grasslands in North America, South

America, Africa, Australia, and Tibet and Inner Mongolia of China have similar surface albedo values. However, grasslands in Northern Canada and Kazakhstan, especially

Kazakh Steppe have much higher surface albedo values. Northern Canada has a subarctic climate. For more than half of the year, much of Northern Canada is snow and ice-covered, resulting a high albedo value over this region. As for Kazakh Steppe, it occupies vast areas in Northern and Central Kazakhstan, extending for more than 2,200 km between the

Caspian Sea in the west and the Altai Mountains in the east and holding one of the largest intact dry steppe areas in the world, of which over 30% is believed to remain in semi- natural or natural condition. Therefore, surface albedo in this area is higher than in other areas.

Another example is the day of year 233 of summer period (Northern Hemisphere), the zonally averaged MODIS actual surface albedo over 80° S to 80° N was 0.11 ± 0.05, 0.14

± 0.04, 0.17 ± 0.05, and 0.16 ± 0.04 (mean ± SD) for ENF, DBF, GRA, and CRO, respectively (Figure 2.8). Among all vegetated land covers, surface albedo at high latitudes in the Southern Hemisphere is considerably higher than at other latitudes. Overall, all four vegetated land covers have a consistent albedo range over latitudes during summer time, except the Sahel in Africa with an extremely high surface albedo compared with other regions.

Figure 2.8 Latitudinal comparisons of zonally averaged MODIS surface albedo among four vegetated land covers, including ENF, DBF, GRA and CRO along the latitude range of 80° S to 80° N around the day 233 of the year. The zoning averaging was performed using MODIS Land Cover product.

Globally averaged surface albedo in the Northern Hemisphere is slightly higher than in the

Southern Hemisphere among all vegetation except the high latitudes. According to Figure

2.9, they all have small albedo ranges and no considerable regional variation can be observed.

Figure 2.9 Spatial patterns of MODIS surface albedo around the day 233 of the year among four vegetated land covers, including ENF, DBF, GRA and CRO. The albedo of each vegetation was extracted by MODIS Land Cover product.

Among all vegetation types, actual surface albedo in high latitude is substantially higher than in mid- and low- latitude, indicating the critical role of snow in determining surface albedo. According to Figure 2.10, the yearly averaged albedo has a spatial pattern as high albedo at high latitudes and low albedo at mid and low latitudes.

Figure 2.10 Spatial patterns of yearly averaged MODIS surface albedo among four vegetated land covers, including ENF, DBF, GRA and CRO. The albedo of each vegetation was extracted by MODIS Land Cover product.

As shown in Figure 2.11, albedo depicted on each date represents the zonal mean value of

MODIS blue-sky albedo averaged first temporally from Year 2000 through Year 2012 and then zonally over 180ºW – 180ºE. Large albedos are observed during snow seasons at high latitudes. 40

Figure 2.11 Latitudinal and seasonal variations in surface albedo over 80° S to 80° N across the globe for the four major vegetated land covers, including ENF, DBF, GRA, and CRO. The albedo of each vegetation was extracted by MODIS Land Cover product.

2.3.2 Shortwave radiative forcing

The conversions from CRO or GRA to DBF generally yielded smaller radiative forcing than did the conversions to ENF, as expected from the observed albedo differences between 41

vegetation types, which is 1.53 W/m2, 0.61 W/m2, 0.74 W/m2, and 0.58 W/m2 for land conversion from GRA to ENF, GRA to DBF, CRO to ENF, and CRO to DBF, respectively.

The temporal variation in TOA shortwave radiative forcing induced by changes in albedo for land conversions is shown in Figure 2.12.

Figure 2.12 Temporal variation in shortwave radiative forcing (W/m2) at the top of the atmosphere (TOA) induced by changes in albedo for land conversions from GRA and CRO to ENF and DBF.

The daily shortwave radiative forcing varied considerably across the seasons for four land conversion scenarios. Land conversion from GRA to ENF would induce the most radiative forcing compared with the other three. In addition, the spatial patterns in shortwave forcing 42

at the TOA induced by changes in albedo for land conversions are shown in Figure 2.13.

A strong spatial dependence was evident in the TOA radiative forcing induced by the non- forest (GRA and CRO) to forest (ENF and DBF) transitions, as determined by spatial patterns in albedo difference and atmospheric opacity.

(a) (b)

Figure 2.13 Spatial patterns in shortwave radiative forcing (W/m2) at the top of the atmosphere (TOA) induced by changes in albedo for land conversions from GRA and CRO to ENF (left column), and GRA and CRO to DBF (right column).

Specifically, taking land replacement between GRA and ENF as an example, a considerable regional variation can be noticed from Figure 2.13 (a). Large radiative forcing was more frequently observed at high latitudes or in the Western American and Central

Asian, but the overall latitudinal dependence was weak. The control of the atmosphere on radiative forcing was revealed such that many regions yielding large radiative forcing coincided with areas that have large fractions of surface contributions to TOA albedo or small atmospheric attenuation.

Chapter 3 Shadow price of albedo using DICE/RICE model

3.1 Introduction

Forestry activities and forest management can affect the planet’s atmosphere and global and regional climate in a variety of ways. Forests help mitigate climate change by sequestering atmospheric carbon, and climate mitigation policies and projects have emphasized the carbon sequestration and storage by forests (Anderson et al., 2011, Bonan,

2008, Canadell and Raupach, 2008, Jackson et al., 2008). However, boreal and high- latitude temperate forests may also contribute to global warming due to the albedo effect, especially when a layer of snow is present (Betts, 2000, Lee et al., 2011). Accordingly, the cooling or warming effect from forestry activities and forest management such as deforestation, afforestation and reforestation in boreal areas is largely dependent on the tradeoff result from carbon sequestration and its conflicting albedo effect. Thus, quantitatively and effectively characterizing and valuing albedo as a climate-regulation service is of great importance to combat climate change (Anderson et al., 2011, Anderson-

Teixeira et al., 2012, Lutz and Howarth, 2014).

Research regarding the incorporation of albedo in land use management and forestry practices has advanced in the past several years, though studies vary with respect to the 45

choice of albedo and the methodology by which albedo is calculated and considered (Lutz and Howarth, 2014). As for the choice of albedo, some may use either black-sky (Luyssaert et al., 2014) or white-sky albedo (Peng et al., 2014), while others use the combination of these two (Li et al., 2015, Zhao and Jackson, 2014). Although the choice of specific albedo would have little impact on the analysis, the actual (blue-sky) albedo was calculated and used in this study through the ratio of the direct to diffusive shortwave radiation. When it comes to integrating albedo in forest management and climate-regulation efforts, the most commonly used method was converting albedo to a quantity of forest carbon stock with the same equivalent radiative forcing (Kirschbaum et al., 2011, Lutz and Howarth, 2014).

Carbon emission equivalent is a C density that can be compared against the C sequestration potential of land management to contrast biogeochemical and biophysical effects (Zhao and Jackson, 2014).

In recent years, instead of carbon emission equivalent, an increasing interest lies in how to integrate climate and economy models to value and price albedo independently in economic terms. For example, Lutz and Howarth (2014) placed an economic value on albedo-related shortwave radiation through the use of shadow prices derived from an integrated assessment model and then examined the potential impact of this value on optimal forest rotation in the White Mountain National Forest (WMNF) in 2014. In 2015, they utilized and compared four methodologies for calculating a shadow price for albedo radiative forcing and applied the resulting eight prices to an ecological and economic forest

model to examine the effects on optimal rotation periods on two different forest stands in the WMNF in New Hampshire, USA (Lutz and Howarth, 2015).

As an increasing interest, studies regarding the incorporation of albedo in forest management and climate mitigation policies has been growing and advancing. However, few have attempted to value and price albedo independently in economic terms as an ecosystem service, despite the fact that this type of valuation is importance especially for resource managers and policy makers. Global and regional estimation of economic valuation of albedo is necessary since albedo varies spatially and fluctuates proportionally and temporally. Therefore, independent valuation and pricing is of great importance and can lead to a more accurate estimation of total climatic impacts of ecosystems, and subsequently contribute to forest management and climate mitigation projects.

This work attempts to fill this gap. DICE model (Dynamic Integrated model of Climate and the Economy) was used to derive a shadow price for albedo and carbon. The DICE model, developed by William Nordhaus, is a well-known integrated assessment model of the interactions between climate and the global economy, viewing the economics of climate change from the perspective of neoclassical economic growth theory (Lutz and Howarth,

2014, Nordhaus, 2014). The advantage of this model is that the basic trends and trade-offs can be captured reasonably accurately, and the underlying model is much more transparent and easily modified by researchers (Nordhaus, 2014). In the DICE model, the world or

individual regions are assumed to have well-defined preferences, represented by a social welfare function, which ranks different paths of consumption (Nordhaus and Sztorc, 2013,

Nordhaus, 2010).

In addition, the DICE model is a global model that aggregates different countries into a single level of output, capital stock, technology, and emissions. The RICE model (Regional

Integrated model of Climate and the Economy) is a multi-region version of the DICE model that was updated and developed alongside the DICE model, by Nordhaus and Yang. It includes multiple regions and decision makers to permit analysis of more strategies and protocols (CIESIN, 1995), and the RICE model is essentially the same except that output, population, emissions, damages, and abatement have regional structures for 12 regions.

The regions are the United States, the European Union, Japan, Russia, Eurasia (Eastern

Europe and several former Soviet Republics), China, India, Middle East, Sub-Saharan

Africa, Latin America, OHI (other high-income countries) and Other (other developing countries). Each region is assumed to produce a single commodity, which can be used for consumption, investment, or emissions reductions (Nordhaus and Sztorc, 2013), to have a well-defined set of preferences, and to optimize its consumption, GHG polices, and investment over time (Nordhaus, 2010).

3.2 Data and Method

The method used in this study to estimate the shadow price of carbon and albedo, using the

DICE model, follows the framework of Lutz and Howarth (Lutz and Howarth, 2014, Lutz 48

and Howarth, 2015) who replied on the calculation of a shadow price using the DICE-2007 model. The most updated DICE-2016R model was used for estimations. Specifically, the

DICE model was used to calculate the global social cost of carbon (SCC) and shadow price of albedo. The shadow price for carbon in the DICE-2016R model was discussed in the main manuscript by Nordhaus (Nordhaus, 2017). In DICE-2016R model, the economy is related to climate through several different geophysical forces and was explained through equations simulating the carbon cycle, radiative forcing, climate change, and climate damages (Lutz and Howarth, 2015). The total impacts of carbon emission on temperature and climate damages were calculated by measuring the change in social welfare through the increase in one unit of carbon emission. Social welfare (W ) in DICE-2016R (Equation

3.1) was measured in decadal time periods and was the product of the instantaneous utility function for each time period, U , dependent upon per capita consumption, ct  , and total labor inputs by population, Lt , and the discount factor applied to future utility, Rt 

(Nordhaus and Sztorc, 2013, Nordhaus, 2017).

T max W U c t L t R t (3.1) t1      

As described in Nordhaus (Nordhaus, 2014, Nordhaus, 2017) and in Lutz and Howarth

(Lutz and Howarth, 2014, Lutz and Howarth, 2015), a change in carbon emission influences climate and causes damages to social welfare. Thus, the shadow price of a unit

of carbon emissions, Vtc   , is calculated by measuring the change in social welfare due to

a one-unit increase in carbon emission, Et  , divided by the marginal utility of consumption, as shown in Equation 3.2.

W  W  C() t V (t)  /  (3.2) c E() tC()() tE t

Furthermore, in exactly the same way, the shadow price of albedo was calculated using

Equation 3.3 following the method in Lutz and Howarth (Lutz and Howarth, 2014, Lutz and Howarth, 2015).

W  W  C() t V (t)  /  (3.3) a RF() tC()()t R F t

W Here, represents the reduction in social welfare caused by a one-unit increase in RF() t radiative forcing at time t (i.e., the marginal welfare impact of radiative forcing in period

), accounting for the impacts of this forcing on future temperatures and climate damages, with the social welfare function reflecting the rage of time preference. In a similar vein,

W represents the marginal utility of consumption at time . Dividing by this term C()t converts utility into appropriate monetary units.

3.3 Global social cost of carbon and albedo based on the DICE model

The result of global social cost of carbon and radiative forcing for 40 years (2020-2060) of the simulation is shown in Table 3.1. The first row shows the estimate for the standard

DICE-2016R model with baseline or current climate policy, and the second row shows the

estimate with optimal controls. The baseline can be interpreted as complete inaction and stalemate on climate policies, while the optimal scenario assumes the most efficient climate-change policies; in this context, efficiency involves a balancing of the costs of abatement and the benefits of reduced climate damages. Although unrealistic, this scenario provides an efficiency benchmark against which other policies can be measured (Nordhaus and Sztorc, 2013, Nordhaus, 2010).

Table 3.1 Global social cost of carbon and radiative forcing from DICE model for the year 2020 - 2060

SCC ($ t-1) 2020 2025 2030 2035 2040 2045 2050 2055 2060

Baseline 37.25 44.04 51.62 60.03 69.29 79.44 90.49 102.48 115.42

Optimal 36.72 43.53 51.17 59.70 69.15 79.58 91.04 103.56 117.20

RF (× 10-03 2020 2025 2030 2035 2040 2045 2050 2055 2060 $w-1yr-1)

Baseline 1.01 1.31 1.67 2.09 2.58 3.16 3.83 4.60 5.47

Optimal 0.97 1.24 1.56 1.94 2.37 2.87 3.44 4.08 4.80

(i) Baseline: No climate-change policies are adopted. (ii) Optimal controls: Climate-change policies maximize economic welfare, with full participation by all nations starting in 2010 and without climatic constraints. (iii) The social cost of carbon and radiative forcing is measured in 2010 international US dollars

The social cost of carbon estimated here is $31.23 per ton of CO2 for emissions in 2015 and $37.25 in 2020 with current climate policy, and is predicted to increase and reach as

high as $2496.94 in the year 2370 (Figure 3.1). The social cost of radiative forcing is

1.01×10-3 $w-1yr-1 with current climate policy and 0.97×10-3 $w-1yr-1 with optimal controls in 2020. The highest social cost of radiative forcing is expected to be 0.23 $w-1yr-1 in the year 2400. Overall, the social cost of carbon and radiative forcing along an optimized path is slightly lower than the baseline path.

$3,000 $0.30

Baseline: SCC $2,500 Baseline: RF $0.25 Optimal: SCC

$2,000 Optimal: RF $0.20

$1,500 $0.15 Social Cost Social Cost RF of

Social Cost Social Cost Carbon of $1,000 $0.10

$500 $0.05

$0 $0.00 2015 2115 2215 2315 2415 Year of Simulation

Figure 3.1 Calculation of global social cost of carbon (SCC) and radiative forcing (RF) for DICE-2016R with current policy and optimized emissions path for 500 years of the simulation.

In addition, the spatial patterns of social cost of radiative forcing with baseline and optimized path are shown in Figure 3.2-3.3. A strong spatial dependence was evident in 52

the shadow price of radiative forcing according to the estimation of DICE model, as determined by spatial patterns in albedo difference and atmospheric opacity.

Figure 3.2 Spatial pattern of social cost of radiative forcing with baseline.

Figure 3.3 Spatial pattern of social cost of radiative forcing with optimized path.

Chapter 4 Summary and Conclusion

4.1 Summary

Land use and land cover changes alter the exchange of energy between the land surface and the atmosphere, also modify land surface characteristics, such as surface albedo and roughness, as a consequence, small changes in surface albedo can lead to global temperature changes equivalent to that attributable to the anthropogenic greenhouse gases emissions (Charlson et al., 2005, Juang et al., 2007). Thus, surface albedo is a critical property of the Earth’s surface that affects the global climate by regulating the radiation balance and influencing surface heat flux exchange. Understanding of the climate response from forestry activities and their biogeochemical and biophysical mechanisms are important in formulating policies to optimize climate benefits of forestry or land management activities.

This work examined the biophysical forcing and climatic impacts of land cover changes across the global through combined remotely sensed observations and climate model outputs, focusing on four types of vegetation, including evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), grasslands (GRA) and croplands (CRO) associated with surface albedo and shortwave radiative forcing. Specifically, surface albedo among four 55

vegetation was compared, by calculating radiative forcing for potential land conversions from GRA or CRO to ENF or DBF, and monetizing the shadow price of carbon and albedo using global and regional DICE and RICE models.

4.2 Conclusion

Forests generally have lower albedo than adjacent open lands (i.e., grasslands or croplands).

Snow can play important roles in regulation surface albedo. Forests have much lower winter albedo than adjacent grasslands or croplands where snow is frequent. Large albedos are observed during snow seasons at high latitudes. Consequently, forests induce positive radiative forcing, dampening the cooling effect of carbon sequestration. The global mean albedo-induced radiative forcing was 1.53 W/m2, 0.61 W/m2, 0.74 W/m2, and 0.58 W/m2 for land conversion from GRA to ENF, GRA to DBF, CRO to ENF, and CRO to DBF, respectively, indicating planting DBF is likely to produce stronger cooling benefits than

ENF. Further, the global social cost of carbon and radiative forcing for 2020 is $37.25 per

-3 -1 -1 ton of CO2, 1.01×10 $w yr , respectively.

Overall, results show that forests can exert a negative radiative forcing through carbon sequestration, but they can also exert positive forcing by reducing surface albedo.

Furthermore, the utility of a climate-economic model provides an impetus for exploring and leveraging economic models to support land-cover change and forest management research. This study presents the importance of incorporating biophysical forcing into climate mitigation strategies. 56

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Appendix A. Global map supplement

Figure A.1 Spatial patterns of global surface albedo.

Figure A.2 Spatial patterns of surface albedo of evergreen needleleaf forest (ENF).

Figure A.3 Spatial patterns of surface albedo of deciduous broadleaf forest (DBF).

Figure A.4 Spatial patterns of surface albedo of grasslands (GRA).

Figure A.5 Spatial patterns of surface albedo of croplands (CRO).

Figure A.6 TOA shortwave radiative forcing from GRA conversions to ENF.

Figure A.7 TOA shortwave radiative forcing from CRO conversions to ENF. 72

Figure A.8 TOA shortwave radiative forcing from GRA conversions to DBF.

Figure A.9 TOA shortwave radiative forcing from CRO conversions to DBF.

Figure A.10 Global land use and land cover of 2001.

Figure A.11 Global land use and land cover of 2012. 74

Figure A.12 Global land use and land cover changes between 2001 and 2012.