Modelling the early to mid-Holocene Arctic
climate
by
Marit Berger
September 2013 Technical Reports from Royal Institute of Technology
Department of Mechanics SE-100 44 Stockholm, Sweden
Akademisk avhandling som med tillst˚and av Kungliga Tekniska H¨ogskolan i Stockholm framl¨agges till offentlig granskning f¨or avl¨aggande av teknologie licentiatexamen fredagen den 27 september 2013 kl 10.15 i Kollegiesalen, Brinel-lv¨agen 8, Kungliga Tekniska H¨ogskolan, Stockholm.
©M. Berger 2013
Modelling the early to mid-Holocene Arctic climate
Marit Berger
Linn´e FLOW Centre, KTH Mekanik, Royal Institute of Technology SE-100 44 Stockholm, Sweden
Abstract
In the recent past it has become evident that the Earth’s climate is changing, and that human activity play a significant role in these changes. One of the regions where the ongoing climate change has been most evident is in the Arctic: the surface temperature has increased twice as much in this region as compared to the global average, in addition, a significant decline in the Arctic sea-ice extent has been observed in the past decades. Climate model studies of past climates are important tools to understand the ongoing climate change and how the Earth’s climate may respond to changes in the forcing.
This thesis includes studies of the Arctic climate in simulations of the early and mid-Holocene, 9 000 and 6 000 years before present. Changes in the Earth’s orbital parameters resulted in increased summer insolation as compared to present day, especially at high northern latitudes. Geological data imply that the surface temperatures in the early to mid Holocene were similar to those projected for the near future. In addition, the geological data implies that the Arctic sea ice cover was significantly reduced in this period. This makes the early to mid-Holocene an interesting period to study with respect to the changes observed in the region at present.
Several model studies of the mid-Holocene have been performed through the Paleoclimate Modeling Intercomparison Project (PMIP1 to PMIP3). The simulations have been performed with climate models of varying complexity, from atmosphere-only models in the first phase to fully coupled models with the same resolution as used for future climate simulations in the third phase. The first part of this thesis investigates the simulated sea ice in the pre-industrial and mid-Holocene simulations included in the PMIP2 and PMIP3 ensemble. As the complexity of the models increases, the models simulate smaller extents and thinner sea ice in the Arctic; the sea-ice extent suggested by the proxy data for the mid-Holocene is however not reproduced by the majority of the models.
One possible explanation for the discrepancy between the simulated and reconstructed Arctic sea ice extent is missing or inadequate representations of important processes. The representation of atmospheric aerosol direct and in-direct effects in past climates is a candidate process. Previous studies of deeper time periods have concluded that the representation of the direct and indirect effects of the atmospheric aerosols can influence the simulated climates, and reduce the equator to pole temperature gradient in past warm climates, in better agreement with reconstructions. The second part of the thesis investi-gates the influence of aerosol on the early Holocene climate. The indirect effect
of reduced aerosol concentrations as compared to the present day is found to cause an amplification of the warming, especially in the Arctic region. A better agreement with reconstructed Arctic sea ice extent is thus achieved.
Descriptors: Arctic, early Holocene, mid-Holocene, climate modelling, pa-leoclimate, sea ice, climate change
Modellering av Arktiska klimatet under tidlig och
mid-holocen
Marit Berger
Linn´e FLOW Centre, KTH Mekanik, Kungliga Tekniska H¨ogskolan SE-100 44 Stockholm, Sverige
Sammanfattning
Under senare tid har det blivit uppenbart att jordens klimat h˚aller p˚a att f¨or¨andras, och att m¨ansklig aktivitet spelar en viktig roll f¨or dessa ¨andringar. Ett av de omr˚aden d¨ar den p˚ag˚aende klimatf¨or¨andringen har varit tydligast ¨ar Arktis: temperaturen vid ytan har ¨okat dubbelt s˚a mycket h¨ar j¨amf¨ort med det globala genomsnittet. Dessutom har man observerat en betydande nedg˚ang i havsisens utbredning i Arktis de senaste decennierna. Simuleringar gjorda med klimatmodeller av forntida klimat ¨ar viktiga verktyg f¨or att f¨orst˚a de p˚ag˚aende klimatf¨or¨andringarna och hur jordens klimat p˚averkas av ¨andringar i klimatsystemets drivningar.
Denna avhandling best˚ar av studier av det arktiska klimatet i modellsimu-leringar av tidig och mid-holocen, ca. 9 000 och 6 000 ˚ar f¨ore nutid. F¨or¨an-dringar i jordens bana kring solen resulterade i en ¨okad sommar-solinstr˚alning j¨amf¨ort med nutid, s¨arskilt vid h¨oga nordliga breddgrader. Geologiska data antyder att jordens temperatur vid ytan under denna period kan j¨amf¨oras med dem vi f¨orv¨antar f¨or den n¨armaste framtiden. Vidare indikerar geologiska data att havsisen i Arktisk var kraftigt reducerad under denna period. Detta g¨or tidig till mid-holocen till en intressant period att studera, med avseende p˚a de f¨or¨andringar som f¨or n¨arvarande har observerats i omr˚adet.
Flera modellstudier av mitt-holocen har utf¨orts i de olika faserna av Paleo-climate Modeling Intercomparison Project (PMIP1 till PMIP3). Simuleringarna har utf¨orts med klimatmodeller av varierande komplexitet, fr˚an atmosf¨arsmod-eller i den f¨orsta fasen, till fullt kopplade modatmosf¨arsmod-eller med h¨og rumslig uppl¨osning i den tredje fasen. I den f¨orsta delen av denna avhandling unders¨oks den simulerade havsisen i de f¨orindustriella och mid-holocen simuleringar som in-g˚ar i PMIP2 och PMIP3 ensemblerna. Modellerna simulerar mindre utbredning och tunnare havsis i Arktis i den senare PMIP ensemblen, men fortfarande ˚ ater-skapar inte modellerna generelt den havsisutbredning som de geologiska data indikerar.
En m¨ojlig f¨orklaring till skillnaderna mellan den simulerade och rekon-struerade havsisutstr¨ackningen kan vara att viktiga processer i klimatsystemet saknas eller inte ¨ar tillr¨ackligt v¨al beskrivna i modellerna. Beskrivningen av atmosf¨ariska aerosoler och dess effekter p˚a klimatet ¨ar en m¨ojlig kandidatpro-cess. Fr˚an studier av forntida varma tidsperioder har man dragit slutsatsen att beskrivningen av aerosoleffekterna p˚averkar det simulerade klimatet. Bland an-nat kan man minska temperaturgradienten mellan ekvator och polerna i tidigare varma klimat, vilket b¨attre ¨overensst¨ammer med temperaturrekonstruktioner.
Den andra delen av avhandlingen unders¨oker p˚averkan av aerosoler p˚a klimatet under tidig holocen. Den indirekta effekten som f¨oljer av l¨agre aerosolkoncen-trationer i tidig holocen j¨amf¨ort med i dag, visar sig orsaka en f¨orst¨arkning av uppv¨armningen, s¨arskilt i det arktiska omr˚adet, vilket st¨ammer b¨attre med havsisrekonstruktioner fr˚an denna period.
Preface
This thesis aims to assess modelling of the early to mid Holocene climate, and especially how the models simulate the climate in the Arctic. The first part includes an introduction to climate change, proxy data, paleoclimate modelling and a brief survey of previous modelling studies of the early and mid-Holocene climate. Finally, an introduction to atmospheric aerosols and their influence on the Earth’s climate is included. The second part consist of one journal article and one manuscript.
Paper 1. M. Berger, J. Brandefelt and J. Nilsson, 2013
The sensitivity of the Arctic sea ice to orbitally-induced insolation changes: a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 and 3 simulations. Clim. Past 9, 969-982, doi:10.5194/cp-9-969-2013
Paper 2. M. Berger, H. Struthers, J. Brandefelt, A. Ekman and L. Wei, 2013
Pristine aerosol concentrations, cloud droplet size and early Holocene climate. Manuscript.
Division of work between authors
The main advisor of the project is Prof. Arne V. Johansson (AJ) and co-advisors are PhD Jenny Brandefelt (JB) and Prof. Johan Nilsson (JN). Paper 1
Analysis of the PMIP simulations and writing of the article were carried out by Marit Berger (MB), with the exception of Chapter 3, which were written by JN. JB, JN and AJ provided supervison and feedback on the article. Paper 2
The CESM simulations and analysis of the simulations were carried out by MB, who has also written the manuscript. The CAM-Oslo simulations were carried out by Hamish Struthers (HS). HS also help implementing the changes to the CESM, together with Liang Wei (LW). JB, HS, Annica Ekman (AE) and AJ provided supervision and feedback on the manuscript.
Contents
Abstract iii
Preface vii
Part I 1
Chapter 1. Introduction 3
Chapter 2. Climate change 5
2.1. Natural climate variability 5
2.2. Anthropogenic climate change 5
2.3. Future projections 6
2.4. Climate change in the Arctic 6
Chapter 3. Arctic sea ice in the Holocene 8
3.1. Sea-ice proxies 8
3.2. Sea ice in the early to mid-Holocene 9
Chapter 4. Modelling of past climates 10
4.1. The Paleoclimate Modeling Intercomparison Project 10
4.2. Modelling studies of the Holocene 12
Chapter 5. Atmospheric aerosols 13
5.1. Influence on climate 13
5.2. Atmospheric aerosol in the past 15
Chapter 6. Summary of papers 16
Chapter 7. Conclusion and outlook 21
Acknowledgement 22
Bibliography 23
Part II 29 Paper 1. The sensitivity of the Arctic sea ice to orbitally-induced
insolation changes: a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2
and 3 simulations. 31
Paper 2. Pristine aerosol concentrations, cloud droplet size and
early Holocene climate 61
Part I
Introduction
CHAPTER 1
Introduction
Observations have revealed a warming trend in the global mean surface tem-perature in the last century (e.g. Jones et al. (2012); Hansen et al. (2010)). The warming has been strongest in the northern hemisphere, and the temper-ature has increased more over land than over oceans. The warming trend has also increased towards the latter part of the 20th the century. It is not only the changes in surface temperature that indicate a global warming; changes in precipitation patterns, reduced snow and ice cover and sea level rise have been observed in the recent past (Solomon et al. 2007). Parallel to the observed temperature increase, continuous measurements of the atmospheric CO2
con-centration, starting in the late 1950s, show an increase in the greenhouse gas concentration, which has been attributed to fossil fuel burning (Forster et al. 2007). The link between human activity and the recent warming is supported by model simulations of the recent past climate, showing that without the an-thropogenic forcing the models are not able to reproduce the observed warming (Solomon et al. 2007). The projected changes in the 21st century include a gen-eral increase in the global annual mean surface temperature, decreases in the cryosphere, sea level rise, and increases in draughts, heat waves and flooding events (Meehl et al. 2007).
What humans have been doing for the last 150 years can be seen as a large-scale experiment, in real-time, on the Earth’s climate system. Obviously, this is not the best way to do an experiment, as we are not fully aware of the consequences of the experiment and whether or not we can reset the changes we impose on the system. One approach that can be used to investigate the climate system is by using climate models. The models can be used to elaborate with e.g. the forcing and boundary conditions to the climate system. In this way we can investigate how the climate system (as described by the models) react to both natural changes and changes introduced by human activity. How-ever, there are uncertainties related to the climate model simulations. Some uncertainties are associated to lack of knowledge about processes in the climate system, others are related to uncertainties in the future emission scenarios.
The emissions scenarios in the Special Report on Emission Scenarios (SRES) and later the Representative Concentration Pathways (RCP), both from the Intergovernmental Panel on Climate Change (IPCC), have provided a set of future emission scenarios used for projections of the Earth’s climate for the next decades to centuries. To determine to what extent we can rely on these
4 1. INTRODUCTION
simulations of the future the climates models need to be evaluated. One way of evaluating the models is by simulations of past climates. In simulations of the recent past, the results can be compared to observations to assess how well the models can simulate the present day climate. In simulations of periods more distant in time, results can be compared to reconstructions of past climates. Climate regimes further back in time can be used to test the models’ ability to simulate climates different from that at present.
This thesis deals with the Arctic climate in model simulations of the early to mid-Holocene, 9 000 to 6 000 years before present (9-6 ka BP). The Holocene is the name of the present interglacial (period of warm climate as compared to cold glacial climates), and covers roughly the last 12 000 years. The period 9 to 6 ka BP (often reffered to as the Holocene Thermal Maximum) was the period in the present interglacial with the highest surface temperatures according to proxy data, including the most recent past (Marcott et al. 2013). Proxy data is indirect data, which reflects climate variables such as temperature, precipitaton and sea-extent. Several independent sea-ice proxies from the early to mid-Holocene indicate that the sea ice in the Arctic was significantly less extensive in this period compared to the latter part of the Holocene (Funder et al. 2011; Jakobsson et al. 2010). In the recent past, the Arctic is the region on Earth where the signs of global warming have been most evident, with the annual mean surface temperature in the Arctic increasing twice as much as the global mean (Trenberth et al. 2007) and the recent decline in the Arctic sea-ice extent Stroeve et al. (2012b). Climate models have not been able to simulate this decline in a satisfactory way (e.g. Stroeve et al. (20007, 2012a); Wang and Overland (2012)).
The first paper included in this thesis compares the simulated Arctic sea ice in an ensemble of climate model simulations of the mid-Holocene (from the Paleoclimate Modelling Intercomparison Project (PMIP); see chapter 4.1 for a description of the project). Simulations of both the second and third phase of the PMIP are compared. The second paper investigates the importance of a realistic prescription of the direct and indirect effect of atmospheric aerosols for the correspondence between simulated and reconstructed early Holocene climate. This study was inspired by earlier modelling studies of warm periods in deeper times, which show that reasonable changes in the aerosol effects provide better correspondence to the reconstructions, especially for the Arctic (Kump and Pollard 2008; Heavens et al. 2012; Kiehl and Shields in press).
CHAPTER 2
Climate change
The list of evidences of a changing climate that have been observed in the recent past is long: increased temperatures, changes in precipitation patterns, diminishing snow cover, a declining extent and thinning of the Arctic sea ice, increased melting of glaciers and ice sheets, and increases in sea level (Trenberth et al. 2007). Changes in the Earth’s climate can be caused by natural variations in forcings or by human-induced changes in forcings.
2.1. Natural climate variability
The Earth’s climate is variable. Proxy data obtained from e.g. ice cores and marine sediments reveal large variations in the Earth’s climate in the past. During the Quaternary (past 3 billion years), the climate has changed between cold glacial periods, when large ice sheets and lower sea level have dominated the Earth’s surface, and warm interglacial periods, when vegetation extended further north than at present and the Greenland ice sheet was almost elimi-nated. These variations are associated with variations in external and internal forcing conditions. Processes that influence the climate on long time scales are plate tectonics (∼ 106
years) and variations in orbital parameters (∼ 105-105
years), land ice and large-scale ocean circulation (∼ 1000 years), while changes in atmospheric trace gases influence the climate on intermediate timescales (∼ 101 up to 103 years) and changes in solar activity and aerosols (from volcanic
eruptions) cause short-term variations (∼ years). Natural external drivers of the climate are forcings that are not influenced by the climate itself (e.g. solar forcing). The external forcings can be amplified or dampened by feedbacks, e.g. build-up of ice sheets, changes in vegetation and greenhouse gases.
2.2. Anthropogenic climate change
Human activities such as emissions of greenhouse gases (GHG) and aerosols to the atmosphere, and land use changes influence Earth’s climate. The re-cent observed warming has to a large extent been attributed to the increased concentration of long-lived GHG in the atmosphere. In 2005 the atmospheric concentrations of the three most important GHGs, CO2, CH4 and N2O, were
379 ppm (CO2), 1774 ppb (CH4) and 319 ppb (N2O). These concentrations
can be compared to the corresponding pre-industrial (1750 AD) concentrations of 280 ppm (CO2), 715 ppb (CH4) and 270 ppb (N2O). The present
concen-trations of CO2 and CH4 by far exceeds the concentrations found in ice-core 5
6 2. CLIMATE CHANGE
past 650 000 LGM MH 1750 AD 2005 RF (2005) [W m−2]
CO2 180-300 ppm 185 280 280 ppm 379 ppm 1.66 ± 0.27
CH4 400-700 ppb 350 659 715 ppb 1774 ppb 0.48 ± 0.05
N2O n.a. 200 270 270 ppb 319 ppb 0.15 ± 0.02
Table 2.1: The atmospheric concentration of the most important long-lived GHGs, in terms of radiative forcing, for the past 650 000 years, 21 ka BP (last glacial maximum), 6 ka BP (mid-Holocene), pre-industrial conditions(1750AD) and the recent past (2005). Concentrations are taken from (Forster et al. 2007), except for LGM and MH, which are taken from the PMIP3 website (http://pmip3.lsce.ipsl.fr/). The GHG concentrations for the past 650 000 years are obtained from ice-core measurements.
data for the past 650 000 years (see table 2.1). To determine to what extent the greenhouse gases have contributed to the observed warming one can consider the radiative forcing (RF) associated with the greenhouse gases. The RF is defined in Ramaswamy et al. (2001) as ”the change in net (down minus up) irradiance at the tropopause after allowing for the stratospheric temperature to adjust to radiative equilibrium, but with surface and tropospheric tempera-tures and state held fixed at the unperturbed levels”. The RF is estimated from model simulations. A positive RF indicates that the Earth is gaining energy. The RF due to CO2, CH4and N2O in 2005 are listed in table 2.1, with CO2as
the greenhouse gas that had contributed most to the global warming in 2005.
2.3. Future projections
Climate models project that the warming will continue into the next decades and century (Meehl et al. 2007); the models project furhter increases in surface temperatures, continued acidification of the oceans, changes in precipitation and wind patterns, and a reduction in sea-ice extent in the northern hemisphere. Independent of the emission scenario, all models project a continuation of the warming in the 21th century, with a warming pattern similar to what has been observed in the recent past (Meehl et al. 2007). However, the rate at which the warming will occur and the amplitude of the warming varies between the models and emission scenarios. The main sources of uncertainty in the models are the descriptions of clouds and aerosols. The influence of aerosols on the climate will be discussed further in chapter 5.
2.4. Climate change in the Arctic
The signs of the ongoing global warming have been most evident in the Arc-tic. Observations of the recent past show that the mean surface temperature increase in the northernmost region has been more than twice as large as the global (Trenberth et al. 2007) and a large reduction in sea ice and snow cover has been observed in the region (Lemke et al. 2007). The strong response in
2.4. CLIMATE CHANGE IN THE ARCTIC 7 the Arctic, a phenomenon known as the Arctic Amplification (AA), is seen in observations of the recent climate (Trenberth et al. 2007), future climate pro-jections (Meehl et al. 2007) and in proxy data describing past climates (Miller et al. 2010; Masson-Delmotte et al. 2006).
The stronger response in the Arctic is due to feedback processes that are important in the region; the ice-albedo feedback is believed to be one of the most important causes for the surface based Arctic warming, but also changes in water vapour, cloudiness and atmospheric and oceanic heat transport may be important for the amplified Arctic warming (e.g. Stroeve et al. (20007); Graversen et al. (2008)). The pronounced surface warming suggests that the ice-albedo feedback plays an important role in the AA (Screen and Simmonds 2010). However, this cannot be the only feedback contributing to the AA, as the phenomenon is also observed in simulations with locked surface albedo (Graversen and Wang 2009) and in aquaplanet simulations without sea ice (Langen and Alexeev 2007).
Since the publication of the fourth assessment report from the IPCC (IPCC 2007) the Arctic sea ice has continued to decrease. The smallest ice extent since the satellite record started was observed in September 2012 (Fig. 2.1); the sea ice extent was then 3.63 Mkm2 (Cavalieri et al. 1996, updated yearly). In the
new set of future climate simulations to be included in the fifth assessment report the Arctic sea ice is projected to decline, with several models projecting an ice-free Arctic ocean in summer within the next 30 to 40 years (Wang and Overland 2012; Stroeve et al. 2012a).
120 o W 60o W 0 o 60 o E 120o E 180 oW 75 oN
Figure 2.1: September sea-ice extent (defined by the 15% sea-ice limit) in 2012. The sea ice concentration is generated from brightness temperature from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data. The white area around the pole is not covered by the satellites. Data from National Snow and Ice Data Centre (NSIDC; Cavalieri et al. (1996, updated yearly)).
CHAPTER 3
Arctic sea ice in the Holocene
Continuous observations of the Arctic sea ice started in the late 1970s when the first satellites were launched. Between 1979 and 2006 the decline in the September (March) sea-ice extent has been -9.1 (-2.9) % per decade (Stroeve et al. 20007). Extending the period to 2011 the decline in September sea-ice extent increases to -12.9 % per decade, with a significant decrease in ice older than two years (Stroeve et al. 2012a). In addition to decreased areal extent, the Arctic sea-ice cover has also become thinner (Kwok and Rothrock 2009).
Enhanced knowledge on past sea-ice variations can help to improve the understanding of the rapid decrease in Arctic sea-ice extent observed in the last decades and projected to continue in the current century. The satellite record provides just more than 30 years of observations of the sea ice. The short time period makes it difficult to distinguish the anthropogenic forced variations from natural variations (Stroeve et al. 20007; Kattsov et al. 2010; Kay et al. 2011), although recent findings suggest that the increased CO2 forcing is
responsible for the decline (Notz and Marotzke 2012). To obtain information about the Arctic sea ice variability further back in time one can use proxy data. Examples of proxy data are fossil pollen, insects and aquatic species, and they are obtained from e.g. sediments cores drilled in lakes and ocean beds.
3.1. Sea-ice proxies
Information about past sea-ice extent can be inferred from different sea-ice proxies. Marine sediment cores taken from the central Arctic Ocean and coastal shelf provides the most complete sea-ice proxy, in time and space. The sed-iments contain traces of microorganisms and ice-rafted sedsed-iments, which can give information about the past. Some planktonic organisms live in or on the sea ice, while others require open water (Polyakov et al. 2010). The presence or absence of these organisms in the sediments can directly provide information about the sea ice. Ice rafted debris (IRD) are larger sediment fractions (larger than 63 µm) found in the sediments, which are transported by icebergs and sea-ice floats. The IRD can also be used to identify the ice transport, as differ-ent kinds of minerals and chemical compositions of the IRD can be attributed to different regions in the Arctic (Polyakov et al. 2010).
Other proxies that can be used to infer past sea-ice conditions are the abundance of driftwood, fossils from e.g. bowhead whales, coastal erosion, ice cores, terrestrial proxies and historical data (Polyakov et al. 2010; Funder et al.
3.2. SEA ICE IN THE EARLY TO MID-HOLOCENE 9 2011). The presence of driftwood indicates that perennial sea ice was present, as the driftwood would sink to the bottom of the ocean within a year if not frozen into the sea ice (Funder et al. 2011). The different species of wood also indicate the origin of the driftwood (Funder et al. 2011; Jakobsson et al. 2010).
3.2. Sea ice in the early to mid-Holocene
Proxy data indicate that the sea-ice cover was strongly reduced in large parts of the Arctic Ocean during the early Holocene (Jakobsson et al. 2010), and that there could even have been ice-free conditions in the summer in some regions where the thickest ice is found today. Funder et al. (2011) find that drift-wood and beach erosion suggest that sea-ice extent along the northeast coast of Greenland was significantly reduced from 8.5 to 6 ka BP. Further, driftwood findings indicate less ice at Ellesmere Island prior to 5.5 ka BP (England et al. 2008). For the same time period, sea-ice dependent diatoms indicate less ice also in the Canadian Arctic Archipelago (Belt et al. 2010; Vare et al. 2009) and central Arctic Ocean (Cronin et al. 2010). Caissie et al. (2010) present proxy data, which indicate that the Bering Sea was sea-ice free all year around in the early Holocene. Fossils from Bowhead whales indicate less sea ice in the Cana-dian Arctic Archipelago in the early Holocene (Dyke et al. 1996). However, increased sea-ice cover is indicated for the western Arctic (Chukchi Sea) during the early Holocene from analysis of marine fossils (de Vernal et al. 2005).
CHAPTER 4
Modelling of past climates
Paleoclimate modelling helps us to understand mechanisms of past climates and provides an opportunity to assess the climate model’s ability to simulate climate different from the present (Braconnot et al. 2007a, 2011, 2012). Simulations of paleoclimates can also constrain the climate sensitivity (i.e. temperature response of the climate system caused by a change in the radiative forcing), which can help improve the future climate projections (Rohling et al. 2012; Edwards et al. 2007).
4.1. The Paleoclimate Modeling Intercomparison Project
The Paleoclimate Modeling Intercomparison Project (PMIP) was initiated in the early 1990s with the purpose to assess the climate models ability to simu-late climates radically different from the present and to gain understanding of mechanisms of past climate changes (Braconnot et al. 2012). In the first phase of PMIP the models used in the comparison were atmosphere-only models and atmospheric models coupled to a slab-ocean. In the second phase, the coupled atmosphere-ocean and atmosphere-ocean-vegetation models were included in the comparison. The main result from the two first phases can be found in PMIP (2000; PMIP I) and Braconnot et al. (2007a,b) (PMIP II). The project has now continued into its third phase, and for the first time the paloclimate simulations are included as core simulations in the fifth Coupled Model Inter-comparison Project (CMIP5). The models used in the third ongoing phase, PMIP3, are identical to the models used for the future climate simulations in CMIP5.
Initially, PMIP focused on two past periods: the mid-Holocene (MH; 6 000 years before present; 6 ka BP) and the last glacial maximum (LGM; 21 ka BP). These periods were chosen due to relatively well-known boundary conditions (Braconnot et al., 2011). The MH represents a period in time when the distri-bution of the insolation to the Earth’s surface was different from today due to differences in the orbital configuration. In the mid-Holocene, the summer and annual mean insolation increased in the high latitudes, and the seasonal cycle of insolation at the top of the atmosphere increased in the Northern Hemi-sphere (NH), and decreased in the Southern HemiHemi-sphere (SH)(Braconnot et al. 2007a). The global annual mean insolation in the mid-Holocene is not very different from the present, but the NH receives more insolation during the NH
4.1. THE PALEOCLIMATE MODELING INTERCOMPARISON PROJECT 11 summer (see left panel in Fig.4.1). The concentration of well-mixed greenhouse gases (GHGs) was lower than at present (see table 2.1).
During the LGM, the major differences, as compared to the present, was a strong reduction in GHGs (compare table 2.1 and table 2.1) and differences in surface boundary conditions. The presence of large ice sheets over North America (the Laurentide ice sheet; LIS) and Scandinavia (the Fennoscandian ice sheet; FSIS) results in lower sea level, which exposed vast areas of sea floor. The sea floor and ice sheets both contribute to an increase of the Earth’s albedo. The mid-Holocene and LGM simulations are equilibrium simulations, which means that forcings and boundary conditions for the simulated time period are kept constant while the simulations are run towards equilibrium (i.e. the trends in the simulations are small).
−20 −20 −10 −10 −10 −10 −10 0 0 0 0 10 10 10 10 10 20 20 20 20 20 30 0 0 30 40 J F M A M J J A S O N D −90 −60 −30 0 30 60 90 −20 −20 −20 −20 −10 −10 −10 −10 0 0 0 0 0 0 10 10 10 10 10 20 20 20 20 30 30 30 30 30 −20−10 40 40 0 0 50 60 J F M A M J J A S O N D −90 −60 −30 0 30 60 90
Figure 4.1: Deviations from pre-industrial top of the atmosphere daily mean insolation (W m−2) for mid-Holocene (6k, left) and early Holocene (9k, right).
The climates of the MH and LGM were the focus of PMIP I and II. In PMIP III the project is expanded to include simulations of the last millennium, last interglacial (130-125 ka BP), the mid-Pliocene (3 Ma BP), and the last deglaciation (15 -10 ka BP) (Braconnot et al. 2011). The last millennium experiment is set up to compare natural variability vs. anthropogenic climate change. The main aim of the last interglacial and mid-Pliocene experiments is to assess climate change in the high latitudes/Arctic. The last deglaciation experiment is set up to investigate how fresh water fluxes from melting ice sheets influences the thermohaline circulation.
12 4. MODELLING OF PAST CLIMATES
4.2. Modelling studies of the Holocene
In addition to the time slice simulations of the MH, a few longer transient simu-lations (i.e. simusimu-lations in which the forcing evolves with time) of the Holocene climate evolution have been conducted. Liu et al. (2009) simulated the climate evolution from the LGM to the Bølling-Allerød warming; a transient simulation forced with changes in orbital forcing, greenhouse gases, continental ice sheets, coastlines and freshwater from melting ice sheets. Renssen et al. (2005, 2009) have performed a couple of transient simulations of the Holocene. Their model results sugget that the presence of the Laurentide ice sheet was important for the temporal and spatial pattern of the warming; the ice sheet delayed the warming in parts of the northern hemisphere by thousands of years. The evo-lution of the last part of the Holocene from the mid-Holocene and up to present was simulated by Fisher and Jungclaus (2011). They forced the simulation with changes in orbital parameters only, and found that the seasonal temperature cycle followed the insolation forcing. When comparing the simulation to proxy data, they found a better agreement in winter than summer, and in northern than southern Europe. Fisher and Jungclause argue that the discrepancy be-tween the model and the proxy data could be attributed to the low resolution and/or missing feedbacks or mechanisms in the model. Goosse et al. (2007) use simulations of the early Holocene (8 ka BP) to assess the realism of future projections. They introduce changes in the Rossby radius of deformation, long-wave radiation scheme, ocean and ice albedo, and the vertical diffusivity in the ocean in their ocean model. All variations in the parameters are chosen so that reasonable simulations of the present day climate are obtained. One the basis of their simulations, they exclude the simulations with the largest changes in the 21st century as unrealistic. They also find that the model is more sensitive to the early Holocene forcing than the mid-Holocene forcing, and suggest that this period should be used for standard model tests, rather than the MH.
CHAPTER 5
Atmospheric aerosols
Aerosols are liquids or particles suspended in air. The main atmospheric aerosols are sulphate aerosol, organic carbon (a mix of chemical compounds containing carbon-carbon bounds), black carbon (soot), nitrate aerosols, min-eral dust and sea salt.
Aerosols emanate from natural and anthropogenic sources. Natural aerosols are sulphate aerosols from marine phytoplankton and volcanoes, organic car-bon from natural biogenic emissions, mineral dust from deserts, and sea salt. Anthropogenic aerosols are sulphate aerosols from fossil fuel and biomass burn-ing, organic carbon from fossil and bio fuel burnburn-ing, black carbon from fossil and biomass burning, nitrate from agriculture, and dust from land use changes and industry. Sulphate, organic and black carbon are predominately of anthro-pogenic origin (Haywood and Boucher 2000), whereas dust and sea salt are predominantly of natural origin. The natural aerosols are larger in size, and dominate the mass burden of atmospheric aerosols. Because of their short res-idence time in the atmosphere, aerosols predominantly influence the radiative forcing locally, as opposed to the well-mixed GHGs (IPCC 2007).
Aerosols are important constituents of the climate system, yet their in-fluence on the radiative forcing is still largely uncertain (Forster et al. 2007). The large uncertainty related to the aerosols is linked to the many ways that aerosols can influence the cloud formation and radiative processes (Haywood and Boucher 2000). IPCC (2007) conclude that the net effect of aerosols on the climate is cooling, and that atmospheric aerosols probably have masked some of the warming caused by the GHGs.
5.1. Influence on climate
Aerosols can have both direct and indirect influence on the climate. The direct effect is related to scattering and absorption of radiation by the aerosol parti-cles (Haywood and Boucher 2000), the indirect effect is related to the aerosol’s ability to act as cloud condensation nuclei (CCN) (Lohmann 2006). CCN are necessary in the formation of clouds. If water is to condense and form clouds in clean air, the supersaturation must exceed levels that occur in the atmosphere (Wallace and Hobbs 2006). If there are impurities in the air (e.g. aerosols), which can act as CCN, water can condense and form cloud droplets at much lower supersaturation.
14 5. ATMOSPHERIC AEROSOLS
Direct aerosol effect
The direct aerosol effect describes the scattering and absorption of radiation by the aerosol particles (Forster et al. 2007). The wavelength of the radiation, relative humidity in the atmosphere, the atmospheric loading and geographical distribution of the aerosols, as well as the underlying surface and the aerosol specie are important for the direct aerosol effect (Haywood and Boucher 2000). Scattering aerosols, such as sulphate, have a cooling effect on the climate. Aerosols that partly absorb radiation, like black carbon, have a warming effect. The warming effect is larger if the carbon aerosols are present over highly reflec-tive surfaces (Haywood and Boucher 2000). The direct effect mainly influences the shortwave radiation. However, high concentrations of large aerosols located at high altitude can have significant influence on the longwave radiation budget as well (Forster et al. 2007). The IPCC AR4 estimated the global average total aerosol direct effect to be -0.50 ± 0.40 W m−2, with a medium to low level of
scientific understanding. The indirect effects
The indirect aerosol effect is related to the microphysical properties of clouds, and is often divided into the first and second indirect effects.
The first indirect effect is also known as the Twomey effect (Twomey 1977) or the cloud albedo feedback. As the latter indicates, the effect alters the cloud albedo via changes to the number (and size) of the cloud droplets. If the cloud droplets are smaller and the cloud droplet concentration larger, the droplets will have larger surface area than fewer, although larger, cloud droplets (Lohmann, 2006). Smaller and more numerous cloud droplets will increase the albedo of the clouds, and have a cooling effect on the climate. The cloud droplet number concentration (CDNC) and aerosol number concentration (Na) are related through
CDN C ≈ Nab (5.1)
where the coefficient b ranges from 0.06 to 0.48, depending on the hygroscop-icity of the aerosols (Feingold 2003).
The second indirect effect, or the cloud lifetime effect, describes how the precipitation efficiency depends on the cloud droplet number concentration (Albrecht 1989). The collision between cloud droplets is less efficient for small droplets; this lowers the precipitation efficiency and increases the cloud lifetime (Lohmann 2006).
The IPCC AR4 estimates the RF due to the first indirect effect to range between -0.3 W m−2 to -1.8 W m−2, with -0.7 W m−2 as the best estimate
5.2. ATMOSPHERIC AEROSOL IN THE PAST 15 of the second indirect effect (Haywood and Boucher 2000), and no estimate of this was included in the (IPCC 2007).
5.2. Atmospheric aerosol in the past
From ice core data one can obtain some information about dust and chemical particles in the past, but the information about spatial variations is limited as data is only available from Greenland and Antarctica, and the temporal resolu-tion is low. Contrary to GHG, aerosols are not well mixed in the atmosphere, and a wider spatial coverage of proxy data is needed to reconstruct past concen-trations. It is however reasonable to assume that the concentration of aerosols that are purely anthropogenic or strongly altered by anthropogenic activity, such as SO4 and black carbon, were lower in the past compared to present.
Andreae (2007) argues that pre-human aerosol concentrations could have been similar to the concentrations found over remote ocean regions in the present climate, and that the difference between aerosol concentrations in continental and marine air was smaller in the pre-human atmosphere. This could be the case for past interglacial periods. During glacial periods, on the other hand, the lower sea level due to the increase in land ice volume exposed vast areas of seabed, which increased the land area and consequently the amount of dust in the atmosphere (Lambert et al. 2012).
One discrepancy between paleoclimate simulations and proxy data, espe-cially in simulations of deeper timescales, is that the models are not able to reproduce the equator to pole temperature gradient found in the paleo data. For instance, Goldner et al. (2013) simulates an equator to pole temperature gradient 10oC stronger than the reconstructed gradient in their simulation of
the mid-Miocene climate optimum (17-14.5 Ma BP). In their simulation of the mid-Cretaceous climate (100 Ma BP), Kump and Pollard (2008) were not able to reproduce the high latitude warming found in the proxy data when they forced their model with increased CO2-levels alone.
Several attempts to resolve the discrepancy for deep warm climates have been made. In the study of the mid-Cretaceous climate, Kump and Pollard (2008) simulated the effect of decreased aerosol concentrations on the Ref f
and precipitation efficiency, and found that the changes they introduce im-proves the agreement between the simulated and reconstructed pole-to-equator temperature gradient. Kiehl and Shields (in press) performed a similar exper-iment for the Eocene thermal Maximum (55 Ma BP), and also find that the increased Ref f significantly improve the agreement between simulated and
re-constructed pole-to-equator temperature gradient. Their results indicate that aerosol effects are of significant importance for the difference in past climates as compared to the present.
CHAPTER 6
Summary of papers
Paper 1
The sensitivity of the Arctic sea ice to orbitally-induced insolation changes; a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 and 3 simulations.
The first paper includes analyses and comparisons of the Arctic sea ice in the mid-Holocene and pre-industrial climates as simulated by models in the Paleoclimate Modelling Intercomparison Project phase 2 (PMIP2) and phase 3 (PMIP3). Results from this study is also included in a review paper by Goosse et al. (in press).
A total of 23 model simulations are included in this work. The 11 models from the PIMP2 ensemble are coupled atmosphere-ocean models. The model versions used for the simulations are similar to the versions used for the future projections in the CMIP3 ensemble, except that the PMIP models (for most models) are run with lower resolution. The 12 models in the PMIP3 ensemble are all identical to the models used for the future projections in the CMIP5 ensemble. The models are forced with changes in the orbital parameters and greenhouse gases. The changes in orbital parameters cause the insolation in the Northern (Southern) Hemisphere to increase during summer (autumn) (Figure 2a). The orbital forcing is more important than the reduced forcing caused by the decrease in GHGs, and the result is a warmer mid-Holocene climate compared to the pre-industrial.
The differences in orbital forcing between the mid-Holocene and the present are unevenly distributed over the year, with a strong summer insolation increase and a weak winter decrease in northern high-latitudes. This annual distribution is evident also in the differences in Arctic sea-ice extent and thickness. All models yield a smaller and thinner Arctic summer sea-ice cover in the MH than in the PI control climate. This is analogous to what can be seen in future climate projections. The differences in the winter sea-ice extent and thickness are smaller in amplitude, and the distribution of models with an increase/decrease in the winter sea-ice extent in the MH compared to PI is approximately equal. For future climate projections, in which the atmospheric
6. SUMMARY OF PAPERS 17 greenhouse gas concentrations increase is constant over the year, the sea-ice extent decreases also in winter (Stroeve et al. 2012a).
−2
0
2
4
−2
−1
0
1
2
3
MH−PI temperature north of 60
oN (
oC)
MH−PI annual mean ice extent (Mkm
6
)
MIROC−ESM
HadGEM2−ES
Figure 6.1: Simulated decrease in near surface summer temperature north of 60oN and the reduction in annual mean sea-ice extent for PMIP2 (squares) and
PMIP3 (circles) models.
A sub-set of the mid-Holocene simulations in the PMIP2 and PMIP3 en-sembles predicts open water off the northeast coast of Greenland in the mid-Holocene summer, and hence can provide a fetch for surface waves. This is in broad agreement with recent analyses of sea-ice proxies (Funder et al. 2011; Jakobsson et al. 2010), and indicates that beach-ridges could have formed on the northeast coast of Greenland during the early to mid-Holocene.
The average central Arctic sea-ice thickness differences between the mid-Holocene and the pre-industrial simulations is further compared to the dif-ferences predicted by the simpler thermodynamic sea ice model of Thorndike (1992). North of 80oN the sea-ice thickness response of the models roughly
fol-lows the behaviour predicted by the thermodynamic model, with an assumed increase in sea-ice melt of 0.2 m per year.
Comparing the simulated pre-industrial Arctic sea ice in the two model ensembles, the PMIP3 models generally simulate smaller extent and thinner sea ice than the PMIP2 models. The largest differences between the model en-sembles are found in the summer sea-ice extent; the model mean pre-industrial sea-ice extent for the PMIP2 (PMIP3) ensemble is 16.9 (16.8) Mkm2 for the
18 6. SUMMARY OF PAPERS
The solar forcing in the mid-Holocene changes most at high latitudes during the summer, compared to the present day. The major forcing in the future climate projections is GHG concentrations; with the forcing applied globally and annually. In the future simulation a strong correlation is found between the reduction in sea-ice extent and increase in global annual mean temperature (Winton 2011). Similarly, there is a strong correlation between the increase in summer surface air temperature north of 60oN and the annual mean decrease
of the sea-ice extent, between the PI and MH simulations (Figure 3).
The response to the mid-Holocene forcing for two of the PMIP3 models, HadGEM2-ES and MIROC-ESM, are compared closer. The two models both simulate the recent observed decline in sea ice well, and have a similar re-sponse to the future CO2 forcing. However, the two model responses to the
mid-Holocene change in solar forcing are significantly different, as can be seen in Fig.6.1. The HadGEM2-ES is more sensitive and experiences a more pro-nounced warming and stronger decrease in the minimum sea-ice extent than the MIROC-ESM. One plausible explanation for the difference in sensitivity is that the MIROC has higher cloud fraction in the summer Arctic, which acts to mute the effects of the solar forcing.
The location of the sea-ice edge on the east coast of Greenland moves northward in the PMIP3 ensemble relative to the PMIP2 ensemble. A small sub-set of the mid-Holocene simulations in the PMIP ensembles predicts open water off the northeast coast of Greenland in the mid-Holocene summer, and hence can provide a fetch for surface waves. This is in broad agreement with recent analyses of sea-ice proxies (Funder et al. 2011; Jakobsson et al. 2010), and indicates that beach-ridges could have formed on the northeast coast of Greenland during the early- to mid-Holocene.
Paper 2
Pristine aerosol concentrations, cloud droplet size and early Holocene climate Climate models have had difficulties simulating the equator to pole gradient inferred by proxy data, for past warm climates (e.g. Goldner et al. (2013)). Sensitivity studies of past warm climates suggest that the misrepresentation of aerosol properties might be an explanation for this poor performance. In the second paper, inspired by studies of the Paleocene-Eocene Thermal Maximum (55 Ma BP; Kiehl and Shields (in press)) and the mid-Cretaceous (100 Ma BP; Kump and Pollard (2008)) we investigate the influence of the aerosols, and cloud properties, on the simulated early Holocene climate. The main difference in forcing between the early Holocene and the pre-industrial is the change in insolation, which can be seen in the right panel in Fig.4.1. The amplitude of the early Holocene change in insolation is bigger than for the mid-Holocene (left panel in Fig.4.1). The aerosol effects are investigated in two sensitivity
6. SUMMARY OF PAPERS 19 experiments carried out with the Community Earth System Model version 1 (CESM1) from the National Centre for Atmospheric Research (NCAR).
The first sensitivity experiment (CESM 9k R14) adopts the method in Kump and Pollard (2008); the cloud droplet effective radius is increased to a constant, pristine value, of 14 µm independent of latitude, longitude and altitude. This choice mimics changes in the first indirect aerosol effect only. In contrast to Kump and Pollard, we do not alter the precipitation efficiency in the model, and hence the second indirect aerosol effect.
In the second sensitivity experiment (CESM 9k CAMO) a more sophisti-cated approach is taken to estimate the changes in aerosol concentrations and cloud properties. An atmosphere-only model with an interactive aerosol mod-ule is used to simulate the change in atmospheric aerosols from the present day to the early Holocene. The change is then used to scale the Ref f in the
early Holocene simulation. In addition, the early Holocene aerosol fields sim-ulated with the atmosphere-only model are used in the sensitivity experiment, to mimic the change in the direct effect of aerosols. For a more thorough description of the experimental design the reader is referred to Paper II.
The changes in Ref f introduced in the CESM 9k R14 simulation result
in an increase in the Ref f over the continents and at low altitudes. In the
CESM 9k CAMO simulation, the Ref fis increased mainly in the high northern
latitudes, and at higher altitudes. The changes are larger in the CESM 9k R14 simulation compared to the CESM 9k CAMO.
In both sensitivity experiments, the resulting near surface temperature in-creases, and the increase is more pronounced at high latitudes in the northern hemisphere (Fig.6.2). The temperature increase is strongest at the surface, but extends all the way through the atmosphere. The annual mean warm-ing is approximately twice as large in the CESM 9k R14 simulation than the CEMS 9k CAMO simulations, both at the surface and higher up in the atmo-sphere. Despite the difference in the spatial patterns of the applied changes in forcing, the spatial pattern of the warming is similar in the two sensitivity experiments.
The areas with the most pronounced warming in the two simulations are strongly related to the regions with the strongest reduction in sea-ice con-centration and strongest increase in low-level cloud cover. The effects of the introduced changes in the direct and first indirect aerosol effects on the sea ice are most pronounced in the Barents Sea and in the western Arctic Ocean.
20 6. SUMMARY OF PAPERS
(a) (b)
Figure 6.2: The simulated response in the annual mean near-surface temper-ature for a) CESM 9k R14 minus CESM 9k and b) CESM 9k CAMO minus CESM 9k. Only responses significant at the 95% level are shown. Note the different scale in the two figures.
CHAPTER 7
Conclusion and outlook
This thesis considers climate model simulations of the early and mid-Holocene climate. The Arctic sea ice in the mid-Holocene PMIP2 and PMIP3 simulations have been investigated. The updated model versions used in PMIP3 are found to simulate thinner and smaller sea-ice extents in the Arctic than the PMIP2 models, in agreement with the last three decades of direct observations. Further analysis of two PMIP3 models show that models, with similar representations of present and future projected Arctic sea ice, behave rather differently when simulating the mid-Holocene sea ice. From this we can conclude that even though the initial sea-ice condition is important for the simulations (models with little sea ice in PI simulations have also have little sea ice in the MH simulations), model physics (related to clouds) is also important for the climate model’s response to forcing condition changes.
Further, the sensitivity studies of the early Holocene climate show that the description of aerosols in the climate models may significantly influence the simulated climate. The climate response when altering the effective droplet radii and aerosol concentrations in the early Holocene climate simulations result in climates with a general increase in temperature, and a pronounced Arctic amplification. This is true even if the changes in the forcing are small. The spatial pattern of the warming appears to be less sensitive to the spatial pattern of the forcing than the amplitude of the forcing.
In the studies included in this thesis, only the long-time mean state of the Arctic Holocene climate was studied. Future work will extend the study to include the time evolution and variability of early to mid-Holocene climate. Multi-millenia simulations of early and mid-Holocene climate and of the climate evolution from early to mid-Holocene will be analysed with focus on climate variability on decadal to centennial timescales. Variability on these timescales is found in reconstructions of this period, with a significant cooling event in the Arctic region according to Greenland ice core data occurring around 8.2 ka BP (Alley et al. 1997). The simulated variability will be diagnosed and compared to this event, as well as the variability seen in recent observations from the Arctic region.
Acknowledgement
I would like to thank my supervisors Prof. Arne Johansson and PhD Jenny Brandefelt at the Mechanics department and Prof. Johan Nilsson at the Me-terorology department at Stockholm University (MISU) for interesting discus-sions, good advice and support throughout the three years I’ve been at KTH. I would specially like to thank Jenny, who has been the main supervisor in practice. It hasn’t always been easy being a meteorologist among all the engi-neers, and I have really appreciated your support and encouragement. I’m very grateful for all the help you have given me, especially with this thesis, which had to be reviewed several times during your summer break. I will also like to thank Arne, Johan, Frederik Schenk and Geert Brethouwer for helping out in different ways with this thesis.
I would also like to thank all the people I have come across at the Mechanics department at KTH and at the Meteorology department and the Bolin Centre at Stockholm University. Specially, I would like to mention Hamish Struthers at ITM/NSC, Annica Ekman at MISU and Liang Wei at KTH Mechanics for the collaboration that lead to the second paper. Your knowledge on atmospheric aerosol, climate models and programming have been invaluable for this project. I would also give a special thanks to my office-mates Peter, Werner and Igor, and the floorball team at the department, thanks to you Wednesday has been my favourite day of the week. I would also like to thank Cecilia at MISU, for being a tremendeous support to me for pretty much my entire academic career. Last, but not least, I will like to thank my family and friends back home and in Lund, for their love and support. You’re the best.
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