The Atmospheric Imaging Mission for Northern Regions: AIM-North · The Atmospheric Imaging Mission...

The Atmospheric Imaging Mission for Northern Regions: AIM-North

Ray Nassar

Climate Research Division, Environment and Climate Change Canada, Toronto, ON,

Canada ([email protected])

Chris McLinden

Air Quality Research Division, Environment and Climate Change Canada, Toronto,

ON, Canada

Christopher E. Sioris

Air Quality Research Division, Environment and Climate Change Canada, Toronto,

ON, Canada

C. Thomas McElroy

Department of Earth and Space Science, York University, Toronto, ON, Canada

Joseph Mendonca


Canada

Johanna Tamminen

Earth Observation Research, Finnish Meteorological Institute, Helsinki, Finland

Cameron G. MacDonald

Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada

Cristen Adams

Environmental Monitoring and Science Division, Government of Alberta, Edmonton,

AB, Canada

Accepted to the Canadian Journal of Remote Sensing, special issue on Arctic and Northern Monitoring and Applications

June 2019

Céline Boisvenue

Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria,

BC, Canada

Adam Bourassa

Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK,

Canada

Ryan Cooney

Canadian Space Agency, Saint-Hubert, QC, Canada

Doug Degenstein

Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK,

Canada

Guillaume Drolet

Direction de la recherche forestière, Québec Ministère des Forêts, de la Faune et des

Parcs, Québec, QC, Canada

Louis Garand

Meteorological Research Division, Environment and Climate Change Canada, Dorval,

QC, Canada

Ralph Girard


Markey Johnson

Air Health Science Division, Health Canada, Ottawa, ON, Canada

Dylan B.A. Jones

Department of Physics, University of Toronto, Toronto, ON, Canada


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Felicia Kolonjari


Canada

Bruce Kuwahara

University of Waterloo, Waterloo, ON, Canada

Randall V. Martin

Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS,

Canada

Charles E. Miller

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California,

USA

Norman O’Neill

Université de Sherbrooke, Sherbrooke, QC, Canada

Aku Riihelä

Finnish Meteorological Institute, Helsinki, Finland

Sebastien Roche


Stanley P. Sander

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, U.S.A.

William R. Simpson

University of Alaska Fairbanks, Fairbanks, AK, U.S.A.

Gurpreet Singh


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Kimberly Strong


Alexander P. Trishchenko

Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON, Canada

Helena van Mierlo


Zahra Vaziri


Kaley A. Walker


Debra Wunch

Department of Physics and School of the Environment, University of Toronto, Toronto,

ON, Canada


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Résumé

AIM-North est une proposition de mission satellitaire visant à acquérir des observations de

l’hémisphère nord à une fréquence et une densité sans précédent pour le suivi des gaz à effets

de serre (GES), de la qualité de l’air (QA) et de la végétation. AIM-North serait constituée de

deux satellites placés dans une orbite hautement elliptique permettant d’observer les surfaces

situées entre 40° et 80°N à plusieurs reprises au cours d’une journée. Chaque satellite

transporterait un spectromètre imageur opérant dans le proche infrarouge et l’infrarouge de

courte longueur d’onde pour la mesure du CO2, CH4 et CO, ainsi qu’un spectromètre imageur

opérant dans l’ultraviolet et le visible pour mesurer la qualité de l’air. Ces deux instruments

mesureraient aussi la fluorescence induite par le soleil qui est émise par la végétation. De

plus, un imageur de nuages ferait des observations en temps quasi-réel, fournissant une

information permettant de pointer les satellites sur les régions les moins ennuagées. De

multiples satellites géostationnaires (GEO) pour la mesure de la QA et des GES sont prévus

pour la décennie 2020 mais ceux-ci ne couvriront pas les régions les plus nordiques comme

l’arctique. AIM-North pallierait à ce manque par des observations quasi-géostationnaires des

latitudes nordiques qui chevaucheraient la couverture des satellites GEO, facilitant ainsi

l’inter-comparaison et la fusion des divers jeux de données. Ces données amélioreraient notre

capacité à prévoir la QA des régions nordiques et à quantifier les flux d’espèces de GES et de

QA provenant des forêts, du pergélisol, de la combustion de biomasse et des activités

anthropiques, approfondissant notre compréhension scientifique de ces processus et appuyant

les politiques environnementales.


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The Atmospheric Imaging Mission for Northern Regions: AIM-North

AIM-North is a proposed satellite mission that would provide observations of

unprecedented frequency and density for monitoring northern greenhouse gases

(GHGs), air quality (AQ) and vegetation. AIM-North would consist of two

satellites in a highly elliptical orbit formation, observing over land from ~40-

80°N multiple times per day. Each satellite would carry a near-infrared to

shortwave infrared imaging spectrometer for CO2, CH4, and CO, and an

ultraviolet-visible imaging spectrometer for air quality. Both instruments would

measure solar induced fluorescence from vegetation. A cloud imager would make

near-real time observations, which could inform the pointing of the other

instruments to focus only on the clearest regions. Multiple geostationary (GEO)

AQ and GHG satellites are planned for the 2020s, but they will lack coverage of

northern regions like the Arctic. AIM-North would address this gap with quasi-

geostationary observations of the North and overlap with GEO coverage to

facilitate intercomparison and fusion of these datasets. The resulting data would

improve our ability to forecast northern air quality and quantify fluxes of GHG

and AQ species from forests, permafrost, biomass burning and anthropogenic

activity, furthering our scientific understanding of these processes and supporting

environmental policy.

1. Introduction

The Arctic and adjacent northern high latitude regions comprise an area of Earth

undergoing rapid and unprecedented change. Temperatures in the northern high

latitudes have risen by about three times the global average increase over recent years

and this trend is expected to continue for the future (IPCC, 2013). These temperature

changes are closely-linked to changes in atmospheric composition and vegetation;

therefore, obtaining observations to monitor relevant atmospheric and vegetation

parameters to quantify changes and better understand the relevant processes is of high

importance.


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Both in situ observations and atmospheric remote sensing from space have

improved our understanding of factors that determine global atmospheric composition,

such as surface sources and sinks as well as chemistry. However, in situ monitoring of

the Arctic and adjacent northern regions is a challenge related to the large spatial extent,

sparse infrastructure, remoteness and harsh weather conditions. Remote sensing of

northern regions from space has its own challenges; but many of these challenges can

be overcome with a mission that is optimized specifically for observing the North.

The Atmospheric Imaging Mission for Northern Regions (AIM-North) is a

proposed satellite mission that would provide observations of unprecedented frequency,

density and quality for monitoring greenhouse gases (GHGs), air quality (AQ), clouds

and vegetation productivity in northern regions. AIM-North would use a pair of

satellites in a highly elliptical orbit (HEO) formation, enabling dense observations over

land from ~40-80°N, multiple times per day. The project is a collaborative effort

between Environment and Climate Change Canada (ECCC) and the Canadian Space

Agency (CSA) with interest and involvement from other federal and provincial

government departments, Canadian academia and industry, and numerous international

scientists, with the potential for still broader participation.

AIM-North is undergoing Phase 0 studies for the CSA from 2019-2020. If

approved for further phases, launch would be feasible around 2026. The industrial team

for Phase 0 is composed of ABB Inc., Airbus Defense and Space and MDA Systems

Limited, with consultation support from NASA’s Jet Propulsion Laboratory. Although a

number of previous studies have already been conducted that have led to the AIM-North

mission concept presented here, some key questions regarding the design of the mission

remain unanswered at present. This paper describes the scientific and programmatic

motivation for the AIM-North mission, summarizes some key studies conducted to date,


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describes the observing requirements to meet the scientific objectives, the evolving

mission concept (orbit, instruments and the general mission plan), the expected

validation capacity and finally, presents the link to decision support and the

international context for such a mission.

1.1 Arctic and Boreal Carbon Cycle

The boreal forests comprise ~30% of the world’s forest area and cover much of

North America and Eurasia (Brandt 2009). Boreal forests represent an important carbon

sink of about 0.5 Pg C yr−1 (Pan et al. 2011) equivalent to 17±6% of the global land

CO2 sink (Le Quéré et al. 2015), and they are an important driver of the seasonal cycle

in global atmospheric CO2 levels (Keeling et al. 2015; Forkel et al. 2016). Their

growing season has lengthened due to climate change (Graven et al. 2017; Pulliainen et

al. 2017), while at the same time disturbances such as fire and some types of insect

infestations are increasing (Bond-Lamberty et al. 2007; Veraverbeke et al., 2017; Seidl

et al. 2017; Natural Resources Canada, 2018) and restricting conditions on productivity

are changing. How this combination of factors will alter the carbon balance of boreal

forests in the future is unclear (Gonsamo et al. 2017, Helbig et al. 2017, Maaroufi et al.

2015; USGCRP, 2018).

Permafrost is ubiquitous in the northern high latitudes, spanning an area of

~18.8 million km2. The vast carbon content of the northern permafrost regions has been

estimated at 1672 PgC (Tarnocai et al. 2009), , 1100-1500 PgC (Hugelius et al. 2014) or

1330-1580 PgC (Schuur et al. 2015), with the higher estimates corresponding to almost

twice the mass of carbon in Earth’s atmosphere. As permafrost thaws, microbes

metabolize carbon and it is estimated that ~5-15% (Schuur et al. 2015) of the carbon

will be released to the atmosphere as CO2 or CH4 by ~2100, but the timing, spatial

distribution and CO2:CH4 ratio of future emissions is highly uncertain (Schneider von


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Diemling et al. 2012; Schuur et al. 2015; AMAP, 2015a). This uncertainty is coupled

with changes to CO2 uptake related to changes in vegetation density (greening or

browning) in the Arctic, which have also been reported as the North warms (Lucht et al.

2002, Piao et al. 2006; Pearson et al. 2013; Bhatt et al. 2013; Fraser et al. 2014; Ju and

Masek, 2016; Edwards and Treitz, 2018; Treharne et al. 2018; USGCRP, 2018). It

remains important to reduce uncertainties and better understand these feedbacks on

future climate change.

Anthropogenic impacts on the carbon cycle are also increasing in the northern

high latitudes as a result of new and planned resource extraction projects and other new

anthropogenic activities enabled by a warming climate. Dense and frequent

observations of CO2, CH4 and solar induced fluorescence (SIF, an indicator of

photosynthetic activity) would improve our ability to monitor the carbon balance of

boreal forests and permafrost and enhance our ability to quantify anthropogenic

greenhouse gas emissions at various scales (e.g. Nassar et al. 2017)

AIM-North would provide observations to better quantify these biospheric and

anthropogenic changes and thus would provide support for the Government of

Canada’s ambitions to reduce greenhouse gas emissions and deliver on Canada’s

commitments under the United Nations Framework Convention on Climate Change

(UNFCCC) Paris Agreement.

1.2 Air Quality at Northern Latitudes

Canadian air quality is generally excellent and air quality has been improving over

North America over the past decade. Across North America, emissions of nitrogen

oxides (NOx), sulphur dioxide (SO2), and most other air pollutants (with ammonia being

the exception) have declined in recent decades as a result of emissions regulations on

cars and power plants, including, e.g., the use of flue gas de-sulfurization (FGD), and an


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overall reduction in the use of coal (e.g. Kharol et al. 2017). As a result, there is a

decreasing trend in aerosol that can be detected from satellite observations of aerosol

optical depth (AOD) over south-eastern Canada (e.g. Boys et al. 2014; Keppel-Aleks

and Washenfelder, 2016). These changes in aerosols have led to significant health-

related improvements for Canadians (2000-2011) (Stieb et al. 2015). Meanwhile, trends

in ground-level ozone vary from region to region but tend to be small (Geddes et al.

2009; Pugliese et al. 2014; Tarasick et al. 2016). Nonetheless, exposure to air pollution

in Canada is responsible for a large number of premature mortalities. A 2018

Government of Canada study found 2.8% of all deaths in Canada are a result of outdoor

air pollution. A Canadian Medical Association study estimated that 21,000 deaths can

be attributed to air pollution annually, with an associated cost of $8B (CMA, 2008) and

by 2030, the accumulated cost is forecasted to be $250B (values in Canadian dollars).

The compounds thought to be responsible for the majority of these deaths are ground

level ozone, NO2, and particulate matter (PM). It is this realization that prompted

Canada to formulate an Air Quality Health Index (AQHI) (Gillet et al. 2004), which

uses the concentrations of these three pollutants to define a scale (ranging from 0 to

10+) to help Canadians understand what the air quality around them means to their

health. These pollutants, along with others such as SO2, formaldehyde, and carbon

monoxide (CO), result (directly or indirectly) from combustion. Anthropogenic sources

include fossil fuel combustion, including automobiles and industrial operations,

hydraulic fracturing (”fracking”), smelting, mining and oil sands operations. Perhaps the

world’s largest single source of SO2 is Norilsk, a collection of copper and nickel

smelters, located in Siberia at 69.38°N (Fioletov et al. 2016). Natural sources include

forest fires and volcanic eruptions.

Wildfires are particularly intense sources of air pollution in the spring and summer.

While variable from year to year, there has been an increase in the number and severity


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of fires in Canada over the past half-century and this is expected to continue with the

warming climate (Gillet et al. 2004; Kirchmeier-Young et al. 2017; 2019). Changing

fire regimes in Canada’s boreal forests are expected to result in increasing fire

frequency and severity (Veraverbeke et al. 2017) as well as a northward march in large

fires with associated impacts to air quality, human health and decreased carbon storage

(Bond-Lamberty 2007; Brandt 2009; Kurz 2013). Forecasting the air quality from these

events using state-of-the-science models remains challenging and additional and

detailed observations are required to guide and verify improvements.

Although some air pollution in the Arctic is a result of local sources (Schmale et al.

2018) a significant fraction is the result of long-range transport from both natural and

anthropogenic sources (AMAP, 2015b). Modelling studies show that European and

Asian sources are important for near-surface pollution, while transport from Asia and

North America may occur at higher altitudes (Walker et al. 2012; Xu et al. 2017). Arctic

Haze is a pan-Arctic pollution phenomenon lasting from late winter to early spring (e.g.

Herber et al. 2002). Further, with the accelerating disappearance of multi-year ice,

Arctic sea routes are more readily negotiated by large container ships. In the coming

years, increased marine traffic through the Arctic, in an attempt to reduce fuel used in

long journeys, could impact air quality by increasing ozone, NO2, SO2, and increasing

the abundance of aerosols (Gong et al. 2018) which will likely affect cloud formation.

1.3 Mission Objectives

The broad objectives of AIM-North are:

• Monitor greenhouse gas (GHG) and air quality (AQ) species over northern regions to

quantify natural and anthropogenic sources/sinks in support of policies to mitigate

climate change and improve air quality.


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• Address the gap in northern AQ and GHG spatial and temporal coverage from the

emerging international virtual constellations and provide overlap with geostationary

(GEO) observations over North America, Europe and Asia.

These objectives closely align with the priorities and mandate of the

Government of Canada and drive a number of specific mission science objectives

outlined in the AIM-North Mission Objectives Document (Nassar et al. 2018) and

restated here in Table 1.

2. Observing Requirements

To obtain atmospheric measurements that provide information on processes occurring at

the Earth’s surface-atmosphere interface, such as emissions or uptake, observations with

some sensitivity to the lowest part of the atmosphere are required. For AQ,

observations are typically made of sunlight reflected off the surface of the Earth in the

ultraviolet-visible (UV-vis) spectral range where these gases absorb light with a

characteristic spectral signature. For GHGs, absorption spectra from reflected sunlight

in the near-infrared and shortwave infrared (NIR-SWIR) are used. For both the UV-vis

and NIR-SWIR, only observations during daylight are obtained and the signal strength

is related to the surface albedo. In addition to observing in the correct spectral region,

precision, accuracy, coverage, rapid revisit rates and spatial resolution are all key

considerations in meeting the science objectives identified for AIM-North. Based on

experience from existing greenhouse gas and air quality satellite missions, available

requirements for upcoming missions and some targeted studies, a set of observing

requirements has been identified for AIM-North to direct Phase 0 studies. These

requirements are summarized in Tables 2 to 5, where inn most cases both a ‘Goal’

requirement and ‘Threshold’ requirement are given. The goal is the desired quantity to

achieve if practical, whereas the threshold is a more easily achievable minimum


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requirement.

For all GHGs, AQ species and SIF, the requirement for pixel size is 2x2 km2

(goal) and 4x4 km2 (threshold) with a revisit time of better than 90 minutes (goal) or

180 minutes (threshold) during daylight, when clouds permit, over a mission duration of

3 (goal) to 5 (threshold) years. AIM-North observing requirements are linked to

international standards and/or aligned with the capabilities of future missions planned

by international space agencies. Justification or the origin of each requirement is

provided in the AIM-North Mission Objectives Document (Nassar et al. 2018). The

rapid revisit rate requirements with diurnal sampling (Table 2) will enhance our

understanding of carbon cycle and air quality processes and support monitoring. The

precision, accuracy and spatial resolution requirements (Tables 2-3) will be challenging

to achieve, but will enable both regional-scale applications and anthropogenic point

source or urban emission quantification north of ~40°N.

Perhaps the most demanding requirements are those for the precision and

accuracy of CO2 (Table 3). The requirements for the column-averaged dry-air mole

fraction of CO2 (XCO2) are a 1-σ single observation precision of ~1 ppm or 0.25% (G =

goal) or ~3 ppm or 0.75% (T = threshold). If the single observation precision meets the

threshold but not the goal, improved precision would still achievable by spatial or

temporal binning of observations. The CO2 accuracy requirement goal is a bias of less

than ~0.2 ppm or 0.05%, with a threshold of ~0.6 ppm or 0.15%. Achieving such a low

bias will very likely require a bias correction at the post-processing stage. These

stringent XCO2 requirements are linked to (but not identical to) the requirements for

Essential Climate Variable (ECVs) identified for the Global Climate Observing System

(GCOS) (CEOS-CGMS, 2015). Similarly, the AIM-North AQ observing requirements

are also aligned with international missions.


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AIM-North’s primary SIF observations would be obtained near 758 nm with

high spectral resolution to resolve solar Fraunhofer lines (Frankenberg et al. 2011), but

lower spectral resolution SIF data from the UV-vis may also be acquired, both yielding

multiple observations spanning the daylight diurnal cycle. Observations of GHGs, AQ

species or SIF spanning the diurnal cycle are most informative of the underlying natural

or anthropogenic processes driving the fluxes and furthermore can help to disentangle

the different contributing factors.

Spatially and/or temporally averaging AIM-North data can improve precision

beyond target values for some applications. Alternatively, sequentially combining

multiple images can yield animations of evolving atmospheric composition. No other

satellite mission ever formally proposed could offer equivalent data for atmospheric

composition or vegetation in northern regions.

3. Orbits

Obtaining frequent and sustained space-based observations of the northern high

latitudes presents orbital design challenges.

A number of different Low Earth Orbit (LEO) variations are commonly used by

atmospheric satellites, which generally provide global sampling; however, with a single

satellite, revisit times are on the order of days to weeks. Sentinel 5P TROPOMI

observes multiple AQ species and CH4 from LEO with roughly daily global coverage

(before loss of data due to clouds) using an extremely wide (~2700 km) swath

(Veefkind et al., 2018). With CO2 observations being more demanding, swath widths

tend to be narrower. The CO2 Monitoring Mission (CO2M), a high priority candidate

under consideration in the European Copernicus Programme (Meijer et al. 2018) is the

widest swath LEO CO2 mission currently planned, with competing designs delivering


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swaths of ~200-250 km, thus requiring multiple satellites to achieve revisits of 2-3 days.

Wider swath LEO CO2 satellites remain of interest and the technology needed to

achieve this continue to be studied (Miller et al., 2017), but even with a hypothetical

~1000 km swath, three satellites would be needed to achieve daily revisit rates and far

more for diurnal coverage (~3 hourly during daylight).

Geostationary orbit (GEO) enables much more frequent revisit rates from a

single satellite, but only over a limited region. Modern weather forecasting relies on a

constellation of LEO and GEO satellites to obtain observations with dense and frequent

coverage. At present, satellite observations of atmospheric composition have only been

made from LEO, but by the early 2020s, three GEO missions for air quality: NASA’s

Tropospheric Emissions: Monitoring Pollution (TEMPO) (Zoogman et al. 2016),

Europe’s Sentinel-4 (Ingmann et al. 2012) and Korea’s Geostationary Environmental

Monitoring Spectrometer (GEMS) (Kim et al. 2016; 2017) will be operating,

complemented by a number of LEO missions. This virtual constellation will consist of

satellites from multiple countries coordinated by the Committee on Earth Observation

Satellites (CEOS) Atmospheric Composition Virtual Constellation (AC-VC) group

(CEOS-ACC, 2011). A coordinated constellation architecture is also emerging for

GHGs (Crisp et al. 2018), with many existing and planned LEO missions, NASA’s

GeoCarb as the first GEO GHG mission to launch around 2022 (Moore et al., 2018) and

missions such as the Geostationary Emission Explorer for Europe (G3E /

ARRHENIUS) (Butz et al. 2015) considered by other space agencies. A coordinated

GHG constellation would enable major advances in carbon cycle monitoring, including

both natural and anthropogenic sources, in support of the UNFCCC Paris Agreement.

Since GEO satellites are stationed at a fixed longitude over the equator and

synchronized with Earth’s rotation, they have the ability to view within a region defined

by some viewing zenith angle (VZA) limit. VZA limits differ for the retrieval of


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different constituents, and can range from ~55-70° (Trishchenko et al., 2019a). For

typical a VZA limit of 62°, regions poleward of ~48-54°N/S (depending on longitude)

are out of range. Although increased use of GEO observing has many advantages, it

creates a serious gap in equivalent high frequency coverage of high-latitude areas,

encompassing most of Canada, Russia, Alaska and northern Europe.

Satellite observations from a highly elliptical orbit (HEO) can address this gap,

since near the highest point of an elliptical orbit (the apogee), the satellite moves very

slowly relative to the Earth. A properly designed 2-satellite constellation can thus

provide quasi-geostationary observations of the North. Figure 1 depicts GEO and HEO

viewing geometries using the Alberta oil sands (~57°N) as an example. From GEO, the

VZA for a point at this latitude is at least 65° and the observed atmospheric column is

far from vertical, which causes distorted pixels and is problematic for data quality. From

a HEO near the critical inclination (i=63.435°N), the VZA is more favourable when the

satellite is within a few hours of apogee. Any longitude offset of the location observed

from the satellite longitude also increases the VZA further, compounding the difficulty

of high-latitude viewing from GEO, where the high Arctic is completely out of range.

The World Meteorological Organization (WMO) has long recognized the role

for HEO in providing meteorological observations of the Arctic with its Vision for the

Global Observing System in 2025 (WMO, 2010). This has been expanded in the Vision

for the WMO Integrated Global Observing System in 2040 (WMO, 2018). CEOS has

recognized that HEO missions can address this high-latitude gap for GHG (Crisp et al.

2018) and AQ (CEOS-ACC, 2011) observations and they would have an important role

in future global GHG or AQ constellations. One of the most unique features of AIM-

North is its proposed plan to observe GHGs and AQ from HEO.

The use of HEOs for Earth observation was first suggested by Kidder and von

der Haar (1990). Canada explored the possibility of a HEO mission for the Polar


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Communications and Weather (PCW) mission concept (Garand et al. 2014) as far back

as 2007. The Polar Highly Elliptical Orbit Science (PHEOS) Weather, Climate and Air

quality (WCA) instrument suite (McConnell et al. 2012; LaChance et al. 2012) was

considered by CSA as an enhancement to PCW. PCW and PHEOS-WCA completed

parallel Phase 0 and A studies in 2012. Although PCW and its possible enhancements

are no longer under consideration, these studies spurred interest in the potential for

atmospheric composition observations from HEO that continues today.

A mission concept study (2016-2018) involving CSA, ECCC and an industry

team of ABB Inc., Airbus Defense and Space and MDA Systems Limited explored a

standalone HEO mission for AQ and GHG, which built upon the PHEOS-WCA studies

and this concept has been recognized as AIM-North since November 2017. The

European Commission has resumed studies on a HEO meteorological concept through

the European Space Agency, with Canadian participation, that has many similarities to

the meteorological component of PCW.

HEOs are an entire class of orbits with many possibilities (Trichtchenko et al.

2014; Trishchenko et al. 2016) and the specific HEO option for AIM-North has not yet

been chosen. The Molniya orbit is the most well-known HEO, due to its use by Soviet

communication satellites since the 1960s. The Molniya orbit was investigated for PCW

(Trishchenko and Garand, 2011) and is one possible candidate for AIM-North. A

Molniya orbit with a period of ~12 hours, an inclination of 63.435° (the critical

inclination), an apogee altitude of ~39,800 km and a perigee of ~500 km or higher, has

two apogee positions separated by 180° longitude. Adding a second satellite in the same

orbital plane, 6 hours behind, gives a second ground-track, which together yield apogee

positions every 90° longitude. A three apogee (TAP) orbit has a ~16-hour period and a

ground-track with three apogee positions separated by 120° longitude, which is another

viable option and has the advantage that the two satellites share a single ground track


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with a repeat cycle of 2 days. Figure 2 shows a polar view of potential Molniya and

TAP ground tracks and apogee positions. Although TAP apogees are slightly higher

(43,493 km for eccentricity = 0.55) than for Molniya orbits, the higher perigee (~5000-

10000 km) reduces exposure to proton radiation, since the satellite does not cross

Earth’s van Allen belts (Trishchenko et al. 2011) as with a lower perigee. Other HEO

possibilities include multiple apogee (MAP) orbits, such as a 14-h orbit which has 7

apogees over a period of 12 days, a 15-h orbit giving 5 apogees over a period of 7 days

and an 18-h orbit giving 8 apogees over 3 days (Trichtchenko et al, 2014; Trishchenko

et al, 2016). Additional recommendations on how to reduce total ionizing dose and

therefore extend the mission lifetime for 14-h, 15-h and 16-h HEO constellations are

presented by Trishchenko et al. (2019b).

For each HEO, favourable observations of the North can be made for roughly

2/3 of the orbit (i.e. 8 h of a 12-h orbit or ~10.7 h of a 16-h orbit, etc.) but the exact

observing period depends on orbital parameters and a VZA limit of 60°, which is

similar to the maximum VZA assumed for current and future LEO and GEO missions

for GHGs and AQ. Since the GHG and AQ observations require reflected sunlight,

there is also a solar zenith angle (SZA) requirement of ~80°, and VZA and SZA

together define the path length of the light through the atmosphere (airmass). Potential

coverage from Molniya and TAP orbits (for a given date/time) can be discerned from

Figure 2.

The final selection of a HEO for AIM-North requires consideration of the timing

of apogee with respect to solar illumination. Since the local solar time of the apogee

changes over the course of a year, maintaining appropriate timing with solar

illumination at priority observing times is crucial. When the apogee is near local

midnight, there is a period of a few weeks with few if any viable observations. The

most sensible approach is to set this “blackout” period to occur during the northern


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winter, perhaps January or February. However, for a Molniya orbit, the date of apogee

for a given local time drifts by ~317-329 days per cycle (Trishchenko and Garand,

2011), while for the TAP orbit the drift is ~348-358 days and for an 18-h orbit the drift

can exceed 360 days. An orbit where this cycle is close to one year (repeatable

annually) would offer an advantage. The advantages of HEO for high latitude viewing

are well-established and several HEO orbit options could meet the needs of AIM-North,

however, more detailed studies weighing all the relevant factors for AIM-North, are

required in order to determine an optimal HEO solution.

4. Instruments

Each of the two AIM-North satellites would have two main instruments: an Ultraviolet-

Visible Spectrometer (UVS), which primarily measures air quality parameters; a near-

infrared and shortwave infrared (NIR-SWIR) spectrometer, which primarily measures

GHGs and SIF; plus a small cloud imager. Each of these instruments would have their

own input optics and pointing systems as described in the subsections below, followed

by some potential mission enhancements.

4.1 Ultraviolet-Visible Spectrometer (UVS)

AIM-North would use a nadir-viewing ultraviolet-visible spectrometer (UVS) to

measure reflected sunlight to retrieve O3, NO2, BrO, HCHO, SO2, OClO, CHOCHO,

aerosols and other species for air quality research and operational forecasting. The UVS

would be a dispersive spectrometer spanning 290-786 nm with about 2000 spectral

elements, giving a spectral sampling of ~0.25 nm (before binning 3 spectral samples).

An order sorter filter would be used to deal with the wide spectral range. The input

aperture of the instrument would have a diameter of 70 mm. The UVS would image the

above species with pushbroom scanning, for example, by acquiring ~500 simultaneous

observations every 2.8 seconds using one dimension of a space-qualified focal plane


June 2019

array (FPA), imaging the field of regard every ~60-180 minutes of daylight.

Observation pixels of ~3x5 km2 at the sub-satellite point from apogee, are being

considered, but pixels become smaller when then satellite is below apogee and distort in

each dimension for non-zero VZAs up to a factor of ~2 for VZA=60°. Figure 3 shows

an example of a simplified viewing pattern for the UVS (assuming an apogee around

midday and a TAP orbit), in which the UVS could cover each coloured region in three

east-to-west sweeps.

4.2 Near-infrared and Shortwave Infrared (NIR-SWIR) Spectrometer

AIM-North would image CO2, CH4 and CO by observing spectra of reflected

shortwave infrared (SWIR) and near infrared (NIR) solar radiation. This could

potentially be carried out with either a dispersive spectrometer (similar to the UVS) or

with an imaging Fourier Transform Spectrometer (IFTS). During the mission concept

study, the baseline design was for an IFTS with 4 NIR-SWIR spectral bands using

separate but identical FPAs and the same fore-optics. Preliminary estimates for the SNR

requirements are stated in Table 4, but such values are highly dependent on the specific

spectral band selected, spectral resolution and type of spectrometer (via the instrument

lineshape), so changing any of these factors as the instrument design evolves will also

change the SNR requirements. New air quality and greenhouse gas retrieval studies are

currently underway, which could also lead to refinements to these numbers as Phase 0

progresses.

The interferometer, which is the core of an IFTS, would use two moving mirrors

on a pivot arm, rather than a traditional Michelson interferometer design (one fixed and

one moving mirror). With the two mirrors moving in opposite directions, only half the

displacement is needed to achieve a maximum optical path difference of 4 cm and

spectral sampling of 0.25 cm-1 (resulting in a spectral resolution of ~0.3 cm-1). The IFTS


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interferometer design has LEO spaceflight heritage in Canada’s Atmospheric Chemistry

Experiment (ACE, Bernath et al. 2005), Japan’s Greenhouse Gases Observing Satellite

(GOSAT, Kuze et al. 2009), GOSAT-2 and other missions; however, imaging would be

a new application facilitated by the possibility for longer stare times from a HEO or

GEO vantage point. Internationally, space-based IFTS instruments are already planned

for the mid-IR and longwave IR, including the GEO operational meteorological

Meteosat Third Generation Infrared Sounder (MTG-IRS) from Europe (Tjemkes et al.

2016) and the Geosynchronous Interferometric Infrared Sounder (GIIRS) from China

(Yang et al. 2017). CSA is currently investing in raising the technology readiness level

of Canadian IFTS technology by supporting research by Canadian industry and

academic partners in consultation with ECCC.

Potential AIM-North IFTS pointing and scanning patterns are still being

investigated, with multiple factors such as input aperture size, the dimensions and frame

rate of the FPA and the integration time in the trade space. A recent IFTS configuration

under consideration would use a 200 mm diameter input aperture to image 128×128

pixels on each FPA with a ground sampling distance of 4x4 km2 (at the sub-satellite

point from apogee), then step to a new position. In many ways, this configuration

resembles the design of the proposed GEO-FTS instrument studied by NASA (Key et

al. 2012, Xi et al. 2015).

With an integration time of 60 seconds, the AIM-North IFTS as configured

above would meet the threshold SNR requirements, while integrating for ~180 s would

meet the goal. With a larger input aperture, integration times could be reduced for the

same SNR, but this would lead to a larger instrument overall. Selecting the integration

time must balance the need for acquisition of data with sufficient signal-to-noise ratio

(SNR), with the fact that longer integration times give measurements subject to more

satellite motion relative to the Earth as well as atmospheric motion (clouds and trace


June 2019

gases). The present plan is to select a baseline integration time, but build flexibility into

the mission design such that it can easily be changed during operations, perhaps even

modified for different viewing conditions or observing priorities.

ESA’s SCIAMACHY (Buchwitz et al., 2005), NASA’s Orbiting Carbon

Observatory 2 (OCO-2) (Crisp et al. 2004) and ESA’s Tropospheric Monitoring

Instrument (TROPOMI) have demonstrated the capabilities of dispersive/grating

spectrometers for measuring CO2, CH4, CO and SIF from LEO and NASA’s GeoCarb

will use a grating spectrometer to observe these species from GEO (Moore et al. 2018).

A grating spectrometer is also a possibility for AIM-North and in fact, our dispersive

band selection and spectral sampling in Table 4 is based directly on plans for GeoCarb

(O’Brien et al. 2016). Advantages of a grating spectrometer would include

simplification of the mission design with more commonalities in scanning and hardware

between the UVS and NIR-SWIR spectrometer and even the potential for sharing fore-

optics or merging the two instruments. Advantages of an IFTS relate to the more direct

imaging approach by using both dimensions of an FPA to image, and more importantly

the greater potential for cloud avoidance due to the shape of the field of view (FOV).

Large gains could be obtained with implementation of an intelligent pointing approach,

which our simulations indicate yields a greater improvement for a square FOV than a

narrow and elongated linear one (i.e. pushbroom scanning) with the same number of

pixels. Intelligent pointing depends on cloud data to inform pointing decisions. The

most reliable method of obtaining such data for the North would likely be to include a

cloud imager in the mission, as discussed below.

4.3 Cloud Imager and Intelligent Pointing

At any given moment, about 70% of the Earth is covered by cloud (Stubenrauch et al.

2013). However, since the clearest regions of the world tend to be dry desert regions of


June 2019

the tropics and low latitudes, the northern mid- and high latitudes are cloudier than the

global average. During the Arctic summer, the monthly mean cloud cover may reach as

high as 85% (Kay et al. 2016).

Measurements of atmospheric GHGs and AQ are sensitive to clouds to varying

degrees. Retrieval of CO2 with its high precision and accuracy requirements is among

the most sensitive and thus aggressive filtering is applied to remove data that are

contaminated by clouds for current missions. Clouds can have a direct effect if they are

in the light path, but other cloud effects (scattering into the light path, shadows, etc.) can

also interfere with observations. In OCO-2 version 8 data, only 7-12% of observations

in a given month pass all of the cloud and other data quality filters (Crisp et al. 2017),

meaning that ~90% of observations are lost due to cloud and other factors. Data loss for

GOSAT is similar (Yoshida et al. 2013), but GOSAT-2 uses an intelligent pointing

approach where a cloud imager provides real time information on clear locations within

the field of regard, where the GHG observations have the best chance of success.

GOSAT-2’s intelligent pointing from LEO improves the yield of cloud-free data from

partially cloudy regions. However, a cloud-informed intelligent pointing approach is

expected to be much more powerful from GEO or HEO, since with the larger

instantaneous field of regard due to a much higher satellite vantage point, more pointing

options are available at any given instant. A cloud imager with the capability to observe

the entire Earth disk north of 40°N, every two hours or better, could provide sufficient

information to inform pointing, which would greatly improve the yield of clear-sky

observations of GHGs and possibly also AQ species.

Figure 4 outlines the basic approach to intelligent pointing and its potential.

Figure 5 illustrates the coverage obtained with this approach for a 90-minute period for

an IFTS with some assumed parameters. Studies are underway to better quantify the


June 2019

impact of intelligent pointing from HEO rather than a simple pointing approach.

Preliminary simulations suggest that more than 50% of AIM-North observations would

be cloud-free using intelligent pointing, but a number of factors impact the results, such

as the size and shape of the FOV and the number FOVs per repeat cycle. . Figure 6

shows that for simulated observations from a TAP Orbit (e = 0.55, i = 63.435°, and

apogee at local noon on July 25) a square 120x120 IFTS FOV with 4x4 km2 ground

pixels results in more cloud-free observations than 40 scans with a 360-pixel 5.333 km

x 1080 km dispersive instrument FOV, covering the same total area and number of

pixels in the same amount of time. A more detailed description of our intelligent

pointing studies will be given in a future publication, but this example suggests better

efficiency for a FOV with a low aspect ratio (~1:1) for observing between clouds, hence

the advantage of intelligent pointing for a dispersive instrument may not be as large as

for an IFTS.

Although data to inform pointing would be the main objective of a cloud imager,

higher spatial resolution cloud data could enhance AQ retrievals, hence requirements

for these different applications are separated in Table 2. Clouds are of course also

important for both weather and climate. AIM-North observations of cloud coverage and

potentially also cloud optical thickness and cloud-top height, with frequent revisit and

good spatial resolution would have a number of applications related to weather and

climate. Cloud data could be of use for weather and climate model evaluation, process

studies, or for initialization of forecasts in numerical weather prediction or solar

irradiance forecasts for the rapidly expanding renewable energy industry (Mathiesen et

al. 2018).

4.4 Potential Mission Enhancements

GHG and air quality observations are the primary observing objectives of AIM-North;


June 2019

however, an enhancement to the mission above the baseline is also possible by adding

one or more detector arrays and a cryo-cooler to cover the longwave and mid-wave

infrared regions as proposed for PHEOS-WCA (McConnell et al. 2012, Lachance et al.

2012). This would enable measurements of temperature, water vapour and atmospheric

motion vectors in northern regions, along with numerous AQ species and CO2 and CH4

with mid- to upper tropospheric sensitivity during days and nights and during all

seasons, to support weather forecasting, air quality studies, studies of vegetation carbon

cycling and a number of other applications.

A number of other instruments for Earth observation (or even other applications

like communications or search and rescue) could benefit from access to HEO. Although

these other ideas are not being pursued directly by the AIM-North team, the

accommodation of additional/hosted payloads may be possible and could potentially

strengthen the rationale for the overall mission.

5 Validation Capacity

Validation is a key component of ensuring high accuracy and precision.

Different forms of validation exist, including surface in-situ and surface remote sensing,

aircraft observations and LEO and GEO satellite missions. For validation, the most

direct comparisons can be made with observations that measure the same fundamental

quantities as AIM-North, vertically integrated abundances. Canada currently operates

two certified sites for satellite XCO2, XCH4 and XCO validation in the global Total

Carbon Column Observing Network (TCCON) (Wunch et al. 2011) that use high-

spectral-resolution, solar-viewing ground-based Fourier Transform Spectrometers

(FTSs). Canada has an Arctic site at Eureka, Nunavut (80°N, 86.4°W) and a boreal site

at East Trout Lake, Saskatchewan (54.4°N, 105°W). The FTSs at these sites also

measure a number of other species, while Eureka hosts a wide range of other


June 2019

instruments for both science and validation. Northern high-latitude TCCON sites in

Europe such as Sodankylä (67.4°N, 26.6°E) and others will also contribute to AIM-

North validation. Additional XCO2, XCH4 and XCO validation can be carried out with

lower resolution FTSs, and there are a growing number of such instruments in Canada

and Europe as well as one in Alaska (Frey et al. 2018).

Air quality validation is becoming more standardized with observations from the

Network for the Detection of Atmospheric Composition Change (NDACC) and

Pandonia Network. NDACC uses ground-based FTSs and UV-visible spectrometers for

remote sensing of the atmosphere from a number of sites across the globe. The

Pandonia network carries out ground-based remote sensing of species including NO2

and SO2 using Pandora/Pandora-2S instrumentation. Both networks have a number of

high northern latitude sites in Canada and Europe. For the validation of aerosol optical

depth and some other aerosol properties, Aeronet, and in particular the Aerocan sub-

network with roughly 20 sites well distributed throughout Canada, is well established.

Aircraft observations are also important for validation of air quality data, and in

particular to understand vertical and horizontal gradients, but also in the validation of

more derived products such as emissions from industrial operations.

Existing northern TCCON, NDACC, Pandonia, Aeronet and other validation

sites along with some new sites will be required to assess data quality and ensure that

accuracy targets are met. These ground-based validation stations are critical to mission

success, especially for the carbon cycle, since small spurious gradients can infer

significant fluxes (Miller et al. 2007). Individual stations need to remain in good

working order throughout the entire mission lifetime to assess drifts and systematic bias.

The networks also require a mechanism for ensuring that the measurements are

calibrated to the same absolute scale. TCCON accomplishes this with aircraft and


June 2019

AirCore (Karion et al. 2010) in situ overflights, which are difficult in the Arctic, but

other options such as co-located portable FTSs may provide a path forward.

6. Decision Support and International Context

Anthropogenic emission of CO2 and CH4 related to fossil fuel extraction, combustion or

leakage and emissions due to land use change, have perturbed the natural carbon cycle

elevating atmospheric CO2 and CH4 concentrations above pre-industrial levels.

Developed countries have been obliged to report their emissions annually under the

United Nations Framework Convention on Climate Change (UNFCCC), signed in 1992.

. Emission self-reporting is done according to internationally agreed upon guidelines

(IPCC, 2006) and in the case of fossil fuel combustion, emissions are calculated

primarily using statistical activity data and emission factors.

Atmospheric concentration measurements currently have an extremely limited

role in national inventory reporting, but there is a growing interest in including

observation-based estimates to provide complementary data on emissions. The

UNFCCC Paris Agreement of 2015, expanded the sphere of countries obligated to

report emissions, although detailed reporting requirements (especially for developing

countries) are still being determined. Quantification of anthropogenic CO2 and CH4

emissions using observations from space has the potential to support national emission

reduction goals and the transparency framework of the Paris Agreement. Over 60 CEOS

member agencies CEOS agreed to the Declaration of New Delhi in 2016, which

identified the need for better space-based GHG observations to support emissions

monitoring for the Paris Agreement.


June 2019

Although national reporting under the Paris Agreement will continue in

accordance with approved best practices (IPCC, 2006) and future approved

methodological refinements, satellite observations of XCO2 and XCH4 can play a key

role in quantifying the spatial, temporal and sectoral distribution of CO2 and CH4

emissions and in tracking evidence of emission reductions or the effectiveness of

policies to reduce emissions on the scale of large facilities, municipalities or

provinces/territories. The use of space-based data is consistent with the transparency

framework of the Paris Agreement and could support the Global Stocktake, intended to

track progress towards achieving the goals of the agreement.

The link between space-based observations and GHG emission reduction

strategies is of high interest in Europe (Ciais et al. 2015; Pinty et al. 2017). The

Copernicus CO2 Monitoring Mission (CO2M), a constellation of 3-4 LEO satellites

aiming to be operational by 2025 (Meijer et al. 2018), is a high priority candidate for

Sentinel expansion. In the longer term, Europe’s vision is for an optimized GHG

constellation that includes LEO, GEO and HEO (Pinty et al. 2017), consistent with the

more detailed constellation architecture recommended by CEOS AC-VC (Crisp et al.

2018).

Northern ecosystems are a massive carbon reservoir (Le Quéré et al. 2015) and

the multitude and complexity of interacting processes make accurate carbon budgets

difficult to estimate (Delpierre et al. 2012); however, the storage or release of this

carbon can affect the global GHG balance (Pan et al. 2011). The potential for dense and

frequent observations of SIF, as enabled by HEO offers an unprecedented monitoring

opportunity, to improve our ability to quantify the photosynthetic carbon uptake of

vegetation (Brown 2018) and stability of ecosystems at high latitudes to characterize the

state of the northern carbon reservoir.


June 2019

AIM-North observations of trace gases with high spatial heterogeneity are of

value to detect emission sources (e.g. McLinden et al. 2016a), for air quality assessment

(e.g. Martin, 2008), and for top-down constraints on emissions (e.g. Streets et al. 2013)

by direct analysis or by use in data assimilation. AIM-North observations of NO2, O3,

and aerosol optical depth could also be provided in near-real-time to forecasting centres

such as ECCC and the European Centre for Medium-Range Weather Forecast

(ECMWF) to be assimilated with models in order to improve forecasts of air quality

indicators, such as the AQHI and the UV-index.

Copernicus and ESA have also commissioned studies on Arctic observing needs

and requirements. These studies have focused mainly on meteorological observations,

as in Canada’s past PCW studies, but observations of GHGs and AQ species are

peripherally being considered. Finland has also been a strong proponent of HEO

missions to observe the North based on their statements while chair of the Arctic

Council. With multiple European studies on Arctic observing or HEO missions recently

completed or at various stages of maturity, a partnership may be possible. Scenarios

include hosted payloads in HEO or even merging missions for a more integrated

partnership, which might reduce total cost. Sharing a HEO platform with a

meteorological imager would also remove the need for AIM-North to have its own

dedicated cloud imager, if the data from the full meteorological imager could be

available in real time to support intelligent pointing.

7. Discussion

AIM-North is currently undergoing Phase 0 studies for the CSA in partnership with

ECCC. Although numerous details of the final technical design and the resulting data

remain to be determined, a key aspect of the mission is the plan for a pair of satellites in

a HEO formation. With this approach, AIM-North would observe GHGs, AQ species,


June 2019

clouds and SIF with unprecedented frequency, density and quality over northern land

regions (~40-80°N). These observations would help to improve our ability to quantify

sources and sinks of GHGs and AQ species for Arctic and boreal science,

anthropogenic emissions monitoring and assist in operational air quality forecasting.

With AIM-North’s objectives closely aligned with Government of Canada priorities,

data from this mission would also support evidenced-based decision making for Canada

and its northern partners, in an era where the importance of space-based Earth

observation data continues to gain increasing recognition as an essential method of

monitoring our planet.

Acknowledgements

We thank S. Polavarapu and M. Neish for providing EC-CAS simulations that were

used in this manuscript and related AIM-North simulation studies. A portion of the

research reported in this paper was performed at the Jet Propulsion Laboratory,

California Institute of Technology, under contract with the National Aeronautics and

Space Agency (NASA). We thank NASA for making their MERRA-2 cloud data

publicly available. Lastly, we acknowledge the significant effort of Professor Jack

McConnell from York University, who led the PHEOS-WCA Phase 0 and Phase A

studies, before he passed away in 2013. AIM-North would not exist today without his

past leadership in pursuing a HEO mission to measure atmospheric composition.

References

AMAP, 2015a. AMAP Assessment 2015: Methane as an Arctic climate forcer. Arctic

Monitoring and Assessment Programme (AMAP), Oslo, Norway. vii + 139 pp.

AMAP, 2015b. AMAP Assessment 2015: Black carbon and ozone as Arctic climate

forcers. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway.

vii + 116 pp.

Bernath, P.F., C.T. McElroy, M.C. Abrams, C.D. Boone, M. Butler, C. Camy-Peyret,

M. Carleer, C. Clerbaux, P.-F. Coheur, R. Colin, P. DeCola, M. De Mazière,

J.R. Drummond, D. Dufour, W.F.J. Evans, H. Fast, D. Fussen, K. Gilbert, D.E.


June 2019

Jennings, E.J. Llewellyn, R.P. Lowe, E. Mahieu, J.C. McConnell, M. McHugh,

S.D. McLeod, D. Michelangeli, C. Midwinter, R. Nassar, F. Nichitiu, C.

Nowlan, C.P. Rinsland, Y.J. Rochon, N. Rowlands, K. Semeniuk, P. Simon, R.

Skelton, J.J. Sloan, M.-A. Soucy, K. Strong, P. Tremblay, D. Turnbull, K.A.

Walker, I. Walkty, D.A. Wardle, V. Wehrle, R. Zander, J. Zou (2005),

Atmospheric Chemistry Experiment (ACE): mission overview, Geophysical

Research Letters, 32, L15S01, doi:10.1029/2005GL022386.

Bhatt, U.S. et al. (2013), Recent Declines in Warming and Vegetation Greening Trends

over Pan-Arctic Tundra, Remote Sens. 2013, 5(9), 4229-4254;

https://doi.org/10.3390/rs5094229

Boys, B.L., Martin, R.V., van Donkelaar, A., MacDonell, R., Hsu, N.C., Cooper, M.J.,

Yantosca,R.M., Lu, Z., Streets,D.G., Zhang,Q., Wang,S., Fifteen-year global

time series of satellite-derived fine particulate matter, Environ. Sci. Technol,

10.1021/es502113p, 2014.

Brown, A. Mapping photosynthesis. Nature Climate Change 2018, 8, 559-559,

doi:10.1038/s41558-018-0221-y.

Buchwitz, M., et al. (2005), Atmospheric methane and carbon dioxide from

SCIAMACHY satellite data: initial comparison with chemistry and transport

models, Atmos. Chem. Phys. 5, 941-962, www.atmos-chem-

phys.org/acp/5/941/, SRef-ID: 1680-7324/acp/2005-5-941

Butz, A. et al. (2015), Geostationary Emission Explorer for Europe (G3E): mission

concept and initial performance assessment, Atmos. Meas. Tech., 8, 4719–4734,

2015, www.atmos-meas-tech.net/8/4719/2015/, doi:10.5194/amt-8-4719-2015

Carrer, D., Moparthy, S., Lellouch, G., Ceamanos, X., Pinault, F., Freitas, S., Trigo, I.

(2018). Land Surface Albedo Derived on a Ten Daily Basis from Meteosat

Second Generation Observations: The NRT and Climate Data Record

Collections from the EUMETSAT LSA SAF. Remote Sensing, 10(8), 1262.

Ciais, P. et al. (2015), Towards a European Operational Observing System to Monitor

Fossil CO2 emissions, Final Report from the expert group, October, 2015.

Canadian Medical Association (2008), No breathing room: National illness costs of air

pollution, summary report from the Technical Report of Illness Costs of Air

Pollution (ICAP) Version 3.0, Canadian Medical Association, Ottawa, 36 pages.

http://www.healthyenvironmentforkids.ca/resources/no-breathing-room-costs-

of-air-pollution.


June 2019

CEOS-ACC (2011), A Geostationary Satellite Constellation for Observing Global Air

Quality: An International Path Forward.

CEOS-CGMS (2015), 2015 Update of Actions in The Response of the Committee on

Earth Observation Satellites (CEOS) to the Global Climate Observing System

Implementation Plan 201 (GCOS IP-10), Developed by the CEOS Coordination

Group for Meteorological Satellites (CEOS-CGMS) and submitted to the GCOS

in support of UNFCCC Subsidiary Body on Scientific and Technological

Advice (SBSTA), 2015 May 10.

Crisp, D. et al. (2004), The Orbiting Carbon Observatory (OCO) mission, Advances in

Space Research, 34, 700-709.

Crisp, D. et al. (2017), The on-orbit performance of the Orbiting Carbon Observatory-2

(OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas.

Tech., 10, 59–81, 2017, www.atmos-meas-tech.net/10/59/2017/,

doi:10.5194/amt-10-59-2017.

Crisp, D. et al. (2018), A Constellation Architecture for CO2 and CH4 Monitoring from

Space, A Committee on Earth Observation Satellites (CEOS) Report, Japan.

Delpierre, N.; Soudani, K.; François, C.; Le Maire, G.; Bernhofer, C.; Kutsch, W.;

Misson, L.; Rambal, S.; Vesala, T.; Dufrêne, E. Quantifying the influence of

climate and biological drivers on the interannual variability of carbon exchanges

in European forests through process-based modelling. Agricultural and Forest

Meteorology 2012, 154–155, 99-112, doi:10.1016/j.agrformet.2011.10.010

Edwards, R., P. Treitz (2018), Vegetation Greening Trends at Two Sites in the

Canadian Arctic: 1984-2015, Arctic, Antarctic and Alpine Research, Volume 49,

601-619, https://doi.org/10.1657/AAAR0016-075.

Fioletov, V.E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N., Carn, S., and

Moran, M. D.: A global catalogue of large SO2sources and emissions derived

from the Ozone Monitoring Instrument, Atmos. Chem. Phys., 16, 11497–11519,

https://doi.org/10.5194/acp-16-11497-2016, 2016.

Frankenberg, C., A. Butz, and G. C. Toon (2011), Disentangling chlorophyll

fluorescence from atmospheric scattering effects in O2 A‐band spectra of

reflected sun‐light, Geophys. Res. Lett., 38, L03801,

doi:10.1029/2010GL045896.

Fraser, R.H., Lantz, T.C., Olthof, I. et al. Ecosystems (2014) 17: 1151.

https://doi.org/10.1007/s10021-014-9783-3


June 2019

Frey, M., Sha, M. K., Hase, F., Kiel, M., Blumenstock, T., Harig, R., Surawicz, G.,

Deutscher, N. M., Shiomi, K., Franklin, J., Bösch, H., Chen, J., Grutter, M.,

Ohyama, H., Sun, Y., Butz, A., Mengistu Tsidu, G., Ene, D., Wunch, D., Cao,

Z., Garcia, O., Ramonet, M., Vogel, F., and Orphal, J.: Building the

COllaborative Carbon Column Observing Network (COCCON): Long term

stability and ensemble performance of the EM27/SUN Fourier transform

spectrometer, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-

146, in review, 2018.

Garand, L., A.P. Trishchenko, L. Trichtchenko, R. Nassar (2014), The Polar

Communications and Weather Mission: Addressing remaining gaps in the Earth

Observing System, Physics in Canada, 70(4), 247-254.

Geddes, J. A., J. G. Murphy, and D. K. Wang (2009), Long term changes in nitrogen

oxides and volatile organic compounds in Toronto and the challenges facing

local ozone control, Atmos. Environ., 43(21), 3407–3415,

doi:10.1016/j.atmosenv.2009.03.053.

Gillett, N. P., A. J. Weaver, F. W. Zwiers, and M. D. Flannigan (2004), Detecting the

effect of climate change on Canadian forest fires, Geophys. Res. Lett., 31,

L18211, doi:10.10129/2004GL020876.

Gong, W., Beagley, S. R., Cousineau, S., Sassi, M., Munoz-Alpizar, R., Ménard, S.,

Racine, J., Zhang, J., Chen, J., Morrison, H., Sharma, S., Huang, L., Bellavance,

P., Ly, J., Izdebski, P., Lyons, L., and Holt, R.: Assessing the impact of shipping

emissions on air pollution in the Canadian Arctic and northern regions: current

and future modelled scenarios, Atmos. Chem. Phys., 18, 16653-16687,

https://doi.org/10.5194/acp-18-16653-2018, 2018.

Gonsamo, A.; Chen, J.M.; Colombo, S.J.; Ter-Mikaelian, M.T.; Chen, J. Global change

induced biomass growth offsets carbon released via increased forest fire and

respiration of the central Canadian boreal forest. Journal of Geophysical

Research: Biogeosciences 2017, 10.1002/2016JG003627, n/a-n/a,

doi:10.1002/2016JG003627.

Helbig, M.; Chasmer, L.E.; Desai, A.R.; Kljun, N.; Quinton, W.L.; Sonnentag, O.

Direct and indirect climate change effects on carbon dioxide fluxes in a thawing

boreal forest–wetland landscape. Global Change Biology 2017, 23, 3231-3248,

doi:10.1111/gcb.13638.


June 2019

Hugelius, G., C. Tarnocai, G. Broll, J. G. Canadell, P. Kuhry, and D. K. Swanson, The

Northern Circumpolar Soil Carbon Database: spatially distributed datasets of

soil coverage and soil carbon storage in the northern permafrost regions, Earth

Syst. Sci. Data, 5, 3-13, 2013, https://doi.org/10.5194/essd-5-3-2013

Ingmann, P., B. Veihelmann, A. Grete Straume, Y. Meijer, "The status of

implementation of the atmospheric composition related GMES missions

Sentinel-4/Sentinel-5 and Sentinel-5p," Proceedings of the 2012 EUMETSAT

Meteorological Satellite Conference, Sopot, Poland, Sept. 3-7, 2012, URL:

http://tinyurl.com/oamraoh.

IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared

by the National Greenhouse Gas Inventories Programme, Eggleston H.S.,

Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan.

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of

Working Group I to the Fifth Assessment Report of the Intergovernmental Panel

on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.

Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY,

USA, 1535 pp.

Karion, A., Sweeney, C., Tans, P., and Newberger, T., AirCore: An Innovative

Atmospheric Sampling System, Journal of Atmospheric and Oceanic

Technology, Nov. 2010, doi: 10.1175/2010JTECHA1448.1.

Kay, J. E., L’Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., and Cesana, G. (2016).

Recent advances in Arctic cloud and climate research. Current Climate Change

Reports, 2(4), 159-169.

Keeling, H.C.; Phillips, O.L. The global relationship between forest productivity and

biomass. Global Ecology and Biogeography, (Global Ecol. Biogeogr.) 2007;

Vol. 16, pp 618-631.

Key, R. et al. (2012), The Geostationary Fourier Transform Spectrometer, 2012 IEE

Aerospace Conference, 978-1-4577-0557-1/12/$26.00 ©2012 IEEE.

Kharol, S. K., C. A. McLinden, C. Sioris, M. W. Shephard, V. Fioletov, A. van

Donkelaar, S. Philip, and R. V. Martin (2017), OMI satellite observations of

decadal changes in ground-level sulfur dioxide over North America, Atmos.

Chem. Phys., 17, 5921–5929, doi:10.5194/acp-17-5921-2017.


June 2019

Kidder, S., T. von der Haar (1990), On the Use of Satellites in Molniya Orbits for

Meteorological Observation of Middle and High Latitudes, Journal of

Atmospheric and Oceanic Technology, 7, 517-522.

Kim, J., M. Kim, and M. Choi (2017), Monitoring Aerosol Properties in East Asia from

Geostationary Orbit: GOCI, MI and GEMS, â€˜Air Pollution in Eastern Asia:

An Integrated Perspectiveâ€™, Chapter 15, 323-334, ISSI Scientific Report Ser.

Vol. 16, edited by Idir Bouarar, Xuemei Wang, and Guy Brasseur, Springer,

504pp, ISBN:978-3-319-59488-0, DOI: 10.1007/978-3-319-59489-7

Kim, J., M. Kim, M. Choi, Y. Park, C.-Y. Chung, L. Chang, S. Hoon Lee, Monitoring

atmospheric composition by GEO-KOMPSAT-1 and 2: GOCI, MI and GEMS,

IEEE International Geoscience and Remote Sensing Symposium (IGARSS),

2016. DOI:10.1109/IGARSS.2016.7730063.

Kirchmeier-Young, M.C., F.W. Zwiers, N.P. Gillett, A.J. Cannon (2017), Attributing

extreme fire risk in Western Canada to human emissions, Climatic Change, 144,

365-379.

Kirchmeier-Young, M.C., Gillett, N. P., Zwiers, F. W., Cannon, A. J., Anslow, F. S.:

Attribution of the influence of human-induced climate change on an extreme fire

season. Earth’s Future (accepted Dec 4, 2018), 2019.

Kuze, A., H. suto, M. Nakajima, T. Hamazaki. Thermal and near infrared sensor for

carbon observation Fourier-transform spectrometer on the Greenhouse Gases

Observing Satellite for greenhouse gases monitoring. Applied Optics, 48, 35,

2009.

LaChance, R., J.C. McConnell, C.T. McElroy, N. O’Neill, R. Nassar, H. Buijs, P.

Rahnama, K. Walker, R. Martin, C. Sioris, L. Garand, A. Trishchenko, M.

Bergeron and the PHEMOS Science Team (2012), PCW/PHEOS-WCA: Quasi-

geostationary Arctic measurements for weather, climate and air quality from

highly eccentric orbits, Proceedings of SPIE, Sensors, Systems and Next-

Generation Satellites XVI, 85330O-1, doi:10.1117/12.974795.

Le Quéré, C. et al. Global Carbon Budget 2015. Earth Syst. Sci. Data 2015, 7, 349-396,

doi:10.5194/essd-7-349-2015.

Lucht, W., et al. 2002. Climatic control of the high-latitude vegetation greening trend

and Pinatubo effect. Science, 296, 1687–1689.

Maaroufi, N.I.; Nordin, A.; Hasselquist, N.J.; Bach, L.H.; Palmqvist, K.; Gundale, M.J.

Anthropogenic nitrogen deposition enhances carbon sequestration in boreal


June 2019

soils. Global Change Biology 2015, 10.1111/gcb.12904, n/a-n/a,

doi:10.1111/gcb.12904.

Martin, R.V., Satellite remote sensing of surface air quality, Atmos. Environ., 42, 7823-

7843, 2008.

Mathiesen, P., C. Collier, J. Kleissl (2018), A high-resolution, cloud-assimilating

numerical weather prediction model for solar irradiance forecasting, Solar

Energy, 92, 47-61, https://doi.org/10.1016/j.solener.2013.02.018.

McConnell, J.C., C.T. McElroy, C. Sioris, N. O’Neill, R. Nassar, H. Buijs, P. Rahnama,

K. Walker, R. Martin, L. Garand, A. Trishchenko, M. Bergeron, and the PHEOS

Science Team (2012), PCW/PHEOS Quasi-geostationary viewing of the Arctic

and Environs for Weather, Climate and Air Quality, Proceedings of the

European Space Agency (ESA) Atmospheric Sciences Conference 2012,

Bruges, Belgium.

McLinden, C. A., V. Fioletov, N. Krotkov, C. Li, K. F. Boersma, and C. Adams, A

decade of change in NO2 and SO2 over the Canadian oil sands as seen from

space, Env. Sci. Tech., doi:10.1021/acs.est.5b04985, 2016a.

McLinden, C. A., V. Fioletov, M. W. Shephard, N. Krotkov, C. Li, R. V. Martin, M. D.

Moran, J. Joiner, Space-based detection of missing sulfur dioxide sources of

global air pollution, Nat. Geosci., 9, 496–500, 2016b.

Meijer, Y.M., et al (2018), Copernicus CO2 Monitoring Mission Requirements

Document (MRD), European Space Agency (ESA), Earth and Mission Science

Division, EOP-SM/3088/YM-ym, version 1.0, 2018-04-18.

Miller, C. E., et al. (2007), Precision requirements for space-based XCO2 data, J.

Geophys. Res., 112, D10314, doi:10.1029/2006JD007659.

Miller, C.E., et al. (2017), Capturing Complete Spatial Context in Satellite Observations

of Greenhouse Gases, Imaging Spectrometry XXI, edited by John F. Silny,

Emmett J. Ientilucci, Proc. of SPIE Vol. 9976, 997609, ©2016 SPIE, CCC code:

0277-786X/16/$18, doi: 10.1117/12.2238766.

Moore, B, et al. (2018), The Potential of the Geostationary Carbon Cycle Observatory

(GeoCarb) to Provide Multi-scale Constraints on the Carbon Cycle in the

Americas, Frontiers in Environmental Science, 6, 109,

https://doi.org/10.3389/fenvs.2018.00109

Nassar, R. C. McLinden, C. Sioris (2018), Atmospheric Imaging Mission for Northern

Regions, Mission Objectives Document (MOD), Version 1.1, September 26,


June 2019

2018, prepared for the Canadian Space Agency. ) ftp://ftp.asc-

csa.gc.ca/users/helpdesk/pub/AIM-North_MOD_v1.1_20180926.pdf.

Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B. A., and Crisp, D.

2017. Quantifying CO2 emissions from individual power plants from space.

Geophysical Research Letters, 44. https://doi.org/10.1002/2017GL074702

Nassar, R., C.E. Sioris, D.B.A. Jones, J.C. McConnell (2014), Satellite observations of

CO2 from a Highly Elliptical Orbit (HEO) for studies of the Arctic and boreal

carbon cycle, Journal of Geophysical Research Atmospheres, 119, 2654-2673.

Natural Resources Canada, 2018. The State of Canada’s Forests, Annual Report, 2018.

O’Brien, D.M., I.N. Polonsky, S.R. Utembe, P.J. Rayner (2016), Potential of a

geostationary geoCARB mission to estimate surface emissions fo CO2, CH4 and

CO in a polluted urban environment: case study Shanghai, Atmos. Meas. Tech.,

9, 4633–4654, 2016, www.atmos-meas-tech.net/9/4633/2016/, doi:10.5194/amt-

9-4633-2016.

Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Phillips,

O.L.; Shvidenko, A.; Lewis, S.L.; Canadell, J.G., et al. A Large and Persistent

Carbon Sink in the World’s Forests. Science 2011, 333, 988-993,

doi:10.1126/science.1201609.

Pearson, R.G., et al. 2013. Shifts in Arctic vegetation and associated feedbacks under

climate change. Nature Climate Change, doi/10.1038/nclimate1858.

Piao, S. L., P. Friedlingstein, P. Ciais, L. M. Zhou, and A. P. Chen. 2006. Effect of

climate and CO2 changes on the greening of the Northern Hemisphere over the

past two decades. Geophys. Res. Lett., 33, L23402.

Pinty, B., G. Janssens-Maenhout, M. Dowell, H. Zunker, T. Brunhes, P. Ciais, D. Dee,

H. Denier van der Gon, H. Dolman, M. Drinkwater, R. Engelen, M. Heimann,

K. Holmlund, R. Husband, A. Kentarchos, Y. Meijer, P. Palmer and M. Scholze

(2017) An Operational Anthropogenic CO₂ Emissions Monitoring &

Verification Support capacity - Baseline Requirements, Model Components and

Functional Architecture, doi: 10.2760/08644, European Commission Joint

Research Centre, EUR 28736 EN.

Polavarapu, S.M. et al. (2016), Greenhouse gas simulations with a coupled

meteorological and transport model: the predictability of CO2, Atmospheric

Chemistry and Physics, 16, 12005-12038, www.atmos-chem-

phys.net/16/12005/2016/, doi:10.5194/acp-16-12005-2016


June 2019

Pugliese, S.C., J.G. Murphy, J.A. Geddes, J.M. Wang (2014), The impacts of precursor

reduction and meteorology on ground-level ozone in the Greater Toronto Area,

Atmos. Chem. Phys, 14, 8197–8207, doi:10.5194/acp-14-8197-2014.

Pulliainen, J., Mika Aurela, Tuomas Laurila, Tuula Aalto, Matias Takala, Miia

Salminen, Markku Kulmala, Alan Barr, Martin Heimann, Anders Lindroth, Ari

Laaksonen, Chris Derksen, Annikki Mäkelä, Tiina Markkanen, Juha

Lemmetyinen, Jouni Susiluoto, Sigrid Dengel, Ivan Mammarella, Juha-Pekka

Tuovinen, and Timo Vesala (2017), Early snowmelt significantly enhances

boreal springtime carbon uptake, Proceedings of the National Academy of

Sciences, vol. 114 no. 42, 11081-11086, doi: 10.1073/pnas.1707889114

Schmale, J., Arnold, S. R., Law, K. S., Thorp, T., Anenberg, S., Simpson, W. R., et al.

(2018). Local Arctic air pollution: A neglected but serious problem. Earth's

Future, 6, 1385–1412. https://doi.org/10.1029/2018EF000952

Schneider von Deimling, T. et al. (2012), Estimating the near-surface permafrost-carbon

feedback on global warming, Biogeosciences, 9, 649–665.

Schuur, E.A.G., McGuire, A.D. et al. 2015. Climate change and the permafrost carbon

feedback, Nature, doi:10.1038/nature14338.

Seidl, R.; Thom, D.; Kautz, M.; Martin-Benito, D.; Peltoniemi, M.; Vacchiano, G.;

Wild, J.; Ascoli, D.; Petr, M.; Honkaniemi, J., et al. Forest disturbances under

climate change. Nature Clim. Change 2017, 7, 395-402,

doi:doi:10.1038/nclimate3303.

Sioris, C. E., I. Abboud, V. E. Fioletov, and C. A. McLinden, The AEROCAN sub-

network: twenty years of automated, accurate aerosol monitoring in Canada with

utility for air quality applications, Atmos. Env., 167, 444-457,

doi:10.1016/j.atmosenv.2017.08.044, 2017.

Streets, D.G., et al , Emissions estimation from satellite retrievals: A review of current

capability, Atmospheric Environment, 1011-1042,

doi:10.1016/j.atmosenv.2013.05.051, 2013.

Stuebenrauch, C.J. et al. (2013), Assessment of Global Cloud Datasets from Satellites,

Bulletin of the American Meteorological Society, 1031-1049,

DOI:10.1175/BAMS-D-12-00117.1.

Tarnocai, C., J. G. Canadell, E. A. G. Schuur, P. Kuhry, G. Mazhitova, and S. Zimov

(2009), Soil organic carbon pools in the northern circumpolar permafrost region,

Global Biogeochem. Cycles, 23, GB2023, doi:10.1029/2008GB003327.


June 2019

Tjemkes, S., S. Gigli, and R. Stuhlmann, "MTG-IRS: the instrument, its products and

current user readiness activities," in Light, Energy and the Environment, OSA

Technical Digest (online) (Optical Society of America, 2016), paper JTu1A.2.

https://www.osapublishing.org/abstract.cfm?URI=FTS-2016-JTu1A.2

Treharne R, Bjerke JW, Tømmervik H, Stendardi L, Phoenix GK. 2018. Arctic

browning: Impacts of extreme climatic events on heathland ecosystem CO2

fluxes. Global Change Biology. in press,

https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.14500.

Trichtchenko, L.D., L.V. Nikitina, A.P. Trishchenko, L. Garand (2014), Highly

Elliptical Orbits for Arctic observations: Assessment of ionizing radiation,

Advances in Space Research, 54, 2398-2414,

http://dx.doi.org/10.1016/j.asr.2014.09.012.

Trishchenko, A.P., L. Garand, L.D. Trichtchenko (2011), Three-apogee 16-h Highly

Elliptical Orbit as Optimal Choice for Continuous Meteorological Imaging of

Polar Regions, Journal of Atmospheric and Oceanic Technology, 28, 1407-1422.

Trishchenko, A.P., L. Garand (2011), Spatial and Temporal Sampling of Polar Regions

from Two-Satellite System on Molniya Orbit, Journal of Atmospheric and

Oceanic Technology, 28, 977-992.

Trishchenko, A.P., L. Garand, L.D. Trichtchenko, L.V. Nikitina (2016), Multi-Apogee

Highly Elliptical Orbits for Continuous Meteorological Imaging of Polar

Regions, Bulletin of the American Meteorological Society, January 2016, 19-24,

doi:10.1175/BAMS-D-14-00251.1.

Trishchenko, A.P., L. Garand, L.D. Trichtchenko (2019a), Observing Polar Regions

from Space: Comparison between Highly Elliptical Orbit and Medium Earth

Orbit Constellations, submitted to Journal of Atmospheric and Oceanic

Technology.

Trishchenko, A.P., Trichtchenko, L.D., Garand, L. (2019b): Highly elliptical orbits for

polar regions with reduced total ionizing doze. Advances in Space Research.,

Volume 63, Issue 12, pp. 3761–3767. https://doi.org/10.1016/j.asr.2019.04.005.

USGCRP, 2018: Second State of the Carbon Cycle Report (SOCCR2): A Sustained

Assessment Report. [Cavallaro, N., G. Shrestha, R. Birdsey, M. A. Mayes, R. G.

Najjar, S. C. Reed, P. Romero-Lankao, and Z. Zhu (eds.)]. U.S. Global Change

Research Program, Washington, DC, USA, 878 pp., doi:

10.7930/SOCCR2.2018.


June 2019

https://doi.org/10.1016/j.asr.2019.04.005

Veefkind, P. et al. (2018), Overview of Early Results from TROPOMI on the

Copernicus Sentinel 5 Precursor, 20th EGU General Assembly, EGU2018,

Proceedings from the conference held 4-13 April, 2018 in Vienna, Austria,

p.12216, 2018EGUGA..2012216V.

Veihelmann, B, Meijer Y, Ingmann, P, Koopman, R, Wright, N, Bazalgette Courrèges-

Lacoste G, et al. The Sentinel-4 mission and its atmospheric composition

products, In Proceedings of the 2015 EUMETSAT meteorological satellite

conference, France, 21–25 September 2015.

Walker, T.W., et al. (2012), Impacts of midlatitude precursor emissions and local

photochemistry on ozone abundances in the Arctic, J. Geophys. Res., 117,

D01305, doi:10.1029/2011JD016370.

WMO (2010). The Vision for the Global Observing System (GOS) in 2025, World

Meteorological Organization (WMO), Geneva, Switzerland.

WMO (2018). Vision for the WMO Integrated Global Observing System (WIGOS) in

2040, World Meteorological Organization (WMO), Geneva, Switzerland.

Wunch, D., G.C. Toon, J.-F. L. Blavier, R.A. Washenfelder, J. Notholt, B.J. Connor,

D.W.T. Griffith, V. Sherlock, P.O. Wennberg, The Total Carbon Column

Observing Network, Phil. Trans. R. Soc. A (2011) 369, 2087–2112,

doi:10.1098/rsta.2010.0240

Xi, X., V. Natraj, R.L. Shia, M. Luo, Q. Zhang, S. Newman, S.P. Sander, Y.L.Yung,

(2015), Simulated retrievals for the remote sensing of CO2, CH4, CO and H2O

from geostationary orbit. Atmos. Meas. Tech., 8, 4817–4830, 2015,

www.atmos-meas-tech.net/8/4817/2015/, doi:10.5194/amt-8-4817-2015

Xu, J.-W., R. V. Martin, A. Morrow, S. Sharma, L. Huang, W. R. Leaitch, J. Burkart, H.

Schulz, M. Zanatta, M. D. Willis, D. K. Henze, C. J. Lee, A. B. Herber, and J. P.

D. Abbatt: Source attribution of Arctic black carbon constrained by aircraft and

surface measurements, Atmos. Chem. Phys., 17, 11971-11989, 2017.

Yang, J., Zhang, Z., Wei, C., Lu, F., Guo, Q. Introducting the new generation of

Chinese Geostationary weather satellites, Fengyun-4, Bulletin of the American

Meteorological Society, 2017, 1637-1659.

Yoshida, Y. et al (2013), Improvement of the retrieval algorithm for GOSAT SWIR

XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas. Tech.,

6, 1533-1547, www.atmos-meas-tech.net/6/1533/2013/, doi:10.5194/amt-6-

1533-2013.


June 2019

Zoogman, P., X.T. Liu, R.M. Suleiman, W.F. Pennington, D.E. Flittner, J.A. Al-Saadi,

B.B. Hilton, D.K. Nicks, M.J.Newchurch, J.L. Carr, S.J. Janz, M.R.

Andraschko, A. Arola, B.D. Baker, B.P. Canova, C.C. Miller, R.C. Cohen, J.E.

Davis, M.E. Dussault, D.P. Edwards, J. Fishman, A. Ghulam, G.G. Abad, M.

Grutter, J.R. Herman, J. Houck, D.J. Jacob, J. Joiner, B.J. Kerridge, J.I. Kim,

N.A. Krotkov, L. Lamsal, C. Li, A. Lindfors, R. Martin, C.T. McElroy, C.A.

Mclinden, V. Natraj, D. Neil, C.R. Nowlan, E.J. O׳Sullivan, P.I. Palmer, R.

Pierce, M. Pippin, S L. Alfonso, R. Spurr, J.J. Szykman, O. Torres, J.P.

Veefkind, B. Veihelmann, H. Wang, J. Wang, and K. Chance, Tropospheric

emissions: Monitoring of pollution (TEMPO), J. Quant. Spectrosc. Radiat.

Transfer, 2016. DOI: 10.1016/j.jqsrt.2016.05.008, 2016.


June 2019

Table 1. Specific mission science objectives for AIM-North.

Greenhouse Gas and Carbon Cycle Objectives

GHG-1

Improve our ability to quantify natural and anthropogenic CO2 and CH4 sources and sinks in the Arctic and northern mid-latitudes (~40-80°N) using imaging observations of column CO2 and CH4

a) Improve our understanding of forest CO2 fluxes and the net carbon balance of northern forests

b) Reduce uncertainties in the spatial, temporal and sectoral attribution of CH4 surface emissions

c) Detect and quantify potential acceleration in CO2 and CH4 emission from permafrost

d) Improve estimation of northern anthropogenic CO2 and CH4 emissions at the scale of a municipality or large industrial source

GHG-2 Improve our understanding of northern vegetation health, processes and carbon flux in a changing climate using solar induced fluorescence (SIF) observations

GHG-3 Better quantify emissions and separate CO2 emissions from anthropogenic versus biospheric sources (respiration and wildfires) with the help of CO, NO2, SIF or other supporting observations

GHG-4 Improve representation of the carbon cycle in climate prediction models through an improved knowledge of current carbon fluxes and processes

Air Quality Objectives AQ-1 Better quantify anthropogenic (including agricultural) and wildfire

emissions and their impact on northern air quality (~40-80°N), including understanding the relative contribution from local sources versus long range transport

AQ-2 Better monitor and predict surface air quality (including UV) over Canada, including understanding how episodic events impact air quality and in particular the Air Quality Health Index (AQHI)

AQ-3 Understand how air pollution influences climate change in the Arctic/Subarctic and the extent to which climate change impacts Arctic/Subarctic pollution

AQ-4 Monitor stratospheric ozone and ozone-related compounds in the North

Cloud Objectives C-1 Obtain observations of clouds to inform the pointing of the Greenhouse

Gas and Air Quality instruments

C-2 Obtain high spatial resolution cloud information to be used in determining cloud fractions for trace gas retrievals and to identify small clouds to allow for more accurate aerosol retrievals


June 2019

Table 2. Pixel Size and Revisit Requirements

Parameter Pixel Size Temporal Revisit

AQ and GHGs species, SIF 2x2 km2 (G), 4x4 km2 (T) 60 min. (G), 180 min. (T)

Clouds for pointing 2x2 km2 (G), 10x10 km2 (T) 30 min. (G), 120 min. (T)

Clouds for AQ retrievals 0.25x0.25 km2 (G), 1x1 km2 (T) 15 min. (G), 60 min. (T)

(G) = Goal (An ideal requirement above which further improvements are not necessary) (T) = Threshold (A minimum requirement to ensure mission objectives can be met) Table 3. Precision and Accuracy Requirements

Species Single Observation Precision (1σ)

Accuracy (1 σ maximum allowed bias)

Nominal Spectral Region

Primary CO2 (X)1 0.25% (1 ppm) (G),

0.75% (3 ppm) (T) 0.05% (0.2 ppm) (G), 0.15% (0.6 ppm) (T)

~1600 nm ~2060 nm ~760 nm

CH4 (X)1 0.5% (9 ppb) (G), 1.5% (27 ppb) (T)

0.1% (2 ppb) (G), 0.3% (6 ppb) (T)

~2340 nm ~760 nm

CO (C or X)1 5% (G) 15% (T)

5% (G) 15% (T)

~2340 nm ~760 nm

O3 (SC) 3% (G)

5% (T) 2% (G) 3% (T)

290-345 nm 540-650 nm

O3 (TC) 3% (G) 5% (T)

20% (G) 30% (T)

290-345 nm 540-650 nm

NO2 (SC) 3% (G) 5% (T)

10% (G) 15% (T)

400-470 nm

NO2 (TC) 1.0x1015 cm-2 (G) 1.5x1015 cm-2 (T)

15% (G) 20% (T)

400-470 nm

Aerosol AOD (C)

0.03 + 15% (G) 0.05 + 20% (T)

0.03 (G) 0.05 (T)

(1) 354, 388, 440, 555, 675nm (2) O2 A-band

Cloud COD (C) NR NR Vis (day) IR (day/night)

Secondary Solar Induced Fluorescence (SIF)

0.30 Wm-2 sr-1 µm-1 (G) 0.90 Wm-2 sr-1 µm-1 (T) – requires averaging

NR ~758 nm (high res) 500-780 nm

SO2 (C) 1.0x1016 cm-2 (G) 1.5x1016 cm-2 (T)

2x1015 cm-2 (G) 3x1015 cm-2 (T)

305-345 nm

HCHO (C) NR NR 325-360 nm BrO (C) NR NR 340-370 nm OClO (C) NR NR 360-390 nm CHOCHO (C) NR NR 420-465 nm

Shading denotes species may be required in near real time (X) = column-averaged dry air mole fraction (C) = total vertical column (TC+SC) (TC) = tropospheric vertical column density (SC) = stratospheric vertical column density NR = no requirement


June 2019

Table 4. NIR and SWIR Spectral Bands, Species and Requirements

Species FTS wavelength range (nm)

FTS wavenumber range (cm-1)a

Preliminary FTS SNR

requirement

Dispersive wavelength range (nm)

Dispersive resolution

(nm)

O2 758-762 13118-13192 88 (G), 30 (T) 757.9-772.0 0.0474

CO2 1598-1618 6180-6258 119 (G), 40 (T) 1591.5-1621.2 0.101

CO2 2042-2079 4810-4897 116 (G), 40 (T) 2045.0-2085.0 0.136

CO, CH4b 2301-2380 4195-4345 130 (G), 40 (T) 2300.6-2345.6 0.153

a FTS spectral sampling is 0.25 cm-1 for all bands, which gives a spectral resolution of ~0.30 cm-1 b CO and CH4 are both retrieved from this band, but the SNR requirements are driven by the CO precision requirements.


June 2019

Table 5. UV-Vis Spectral Bands, Species and Requirements

Species Fitting Window Wavelength (nm)

Wavelength (nm) for SNR requirement

SNR Goal

SNR Threshold

Primary Species (single observation SNR requirements are given)

O3 290-345 330 239 144

NO2 400-470 416.4 765 536

O3 540-650 544 650

177 123

106 74

AOD 675 675 36 12

Secondary Species (SNRs can be achieved by binning in space and/or time)

SO2 305-321 320.8 2090 1447

HCHO 325-360 330 2394 1596

BrO 340-370 366 3084 2159


June 2019

Figure 1. (Left) A 16-hr HEO (Three Apogee orbit) with an inclination of 63.435°,

eccentricity of 0.50 and apogee and perigee altitudes of ~41,885 km and ~9709 km. The

number of hours relative to apogee for the satellite in orbit is indicated, showing that for

more than 10 hours of the 16-hr period, the satellite would have a favourable view of

the north. (Right) Lines on the figure show the nadir and ±60° latitude from the sub-

satellite point for GEO and HEO (i=63.435°), while the red dot indicates the Alberta oil

sands at ~57°N.


June 2019

Figure 2. Examples of AIM-North coverage for two potential HEO scenarios. (Left)

The orbit track (black line) for a two-satellite Molniya constellation assuming an

eccentricity of 0.75 and the critical inclination (63.435°N). The satellites reach the four

apogee points at local noon on June 21. The satellite positions (circled dots) and the

viewable region are shown for June 22 at 16:15 UTC. (Right) The orbit track for a two-

satellite TAP orbit constellation assuming an eccentricity of 0.55 and the critical

inclination. For the TAP orbit, both satellites share a ground track and each reach the

three apogee points near local noon on July 15. The satellite positions and viewable

regions are shown for July 16 at16:00 UTC. The viewable region shown in green is that

where both viewing zenith angle (VZA) and solar zenith angle (SZA) are within their

limits of 60° and 80° respectively. Areas with only one of the two requirements satisfied

are shown in yellow (VZA) and orange (SZA) respectively, while for areas in red,

neither requirement is satisfied at this moment in orbit, but would be observed at other

times.


June 2019

Figure 3. A simplified scanning pattern for AIM-North assuming the satellite reaches

apogee around midday and a TAP orbit. The UVS could cover each coloured region in

three west-to-east sweeps while the IFTS could cover the coloured region with a step

and stare approach and a variety of different possible scanning patterns. Since both

instruments observe reflected sunlight, only observations during daylight, over land and

sufficiently cloud-free would be successful.


June 2019

Figure 4. (Left) The red boxes show 124 possible positions for a 128x128 pixel IFTS

field-of-view (FOV) with ground sampling of ~4x4 km2 from AIM-North at an apogee

at 95°W, accounting for pixel growth with larger viewing angles. (Centre) The 45 FOV

locations that AIM-North could point its GHG instrument in a 90-minute period based

on cloud coverage (NASA MERRA2 0.5°x0.67° cloud fractions greater than 0.1 in

white to grey), solar illumination and viewing angles. The satellite position and orbit

tracks are also shown. (Right) An indication of the average number of clear-sky solar-

illuminated hours per day in June 2015, which suggests that the atmosphere over

essentially all northern land is observable if pointing occurs at the proper time, which

would be facilitated by the flexibility of an intelligent pointing approach from HEO, but

not necessarily with the rigid observing schedule obtained from LEO overpass times.


June 2019

Figure 5. a-c) Model XCO2, XCO and XCH4 from the Environment Canada Carbon

Assimilation System (EC-CAS) forward simulations (Polavarapu et al. 2016). d) NASA

MERRA2 cloud data (0.5°x0.67°) showing cloud fractions greater than 0.1 (white to

grey) at the specified time, along with the AIM-North satellite position, orbit track,

illuminated/dark areas of the Earth disk and red squares to show the locations of 45

stares by the AIM-North NIR-SWIR FOV in a 90 minute period. e) The resulting

observational coverage (red) for XCO2, XCO, XCH4 and SIF that would be obtained in

this 90 minute period with an intelligent pointing approach, after accounting for

pointing, clouds and the loss of data over water due to its low albedo.


June 2019

Figure 6. The number of XCO2 observations per month for AIM-North based on an

Observing System Simulation Experiment (OSSE) that assumes two satellites in a TAP

orbit using intelligent pointing with an IFTS and a 120x120 pixel focal plane array with

4x4 km2 ground pixels and a dispersive spectrometer giving 40 scans of 360 pixels,

each 5.33x3 km2, each observing over 120 seconds.


June 2019

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