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CHAPTER FIVE
Integrated Climate
Observing System and
Earth System Analysis
O
bservations are critical for monitoring and advancing understanding of the
processes driving the variability and trajectory of the climate system. The
evaluation and improvement of climate and Earth system models is thus fun-
damentally tied to the quality of the observing system for climate. The observational
assessment of model performance is an important prerequisite for credible climate
predictions and projections and for articulating their uncertainties. Climate observa-
tions and model assessment have become increasingly important and urgent because
climate change is progressing rapidly.
A longstanding issue is the adequacy of the observational record for the purpose of
model evaluation and advancement. Numerous Earth observations are made, but
many are not of sufficient quality to evaluate models or meet other climate needs
(e.g., NRC, 2007). In the atmosphere, most observations are made to initialize weather
forecasts. Because the amplitude of weather fluctuations is large, high measurement
accuracy and low bias have historically not been a priority. In contrast, climate change
must discern relatively small changes over time, which requires stable calibrated mea-
surements of high accuracy. Knowing how the measurements of today relate to those
of years or decades ago is a very important component of climate science.
Another challenge for a climate observing system is that monitoring climate involves
measuring many more variables than for monitoring weather. Space and time scales
are also more extreme for climate—clouds vary rapidly while ice sheets and the deep
ocean vary very slowly. The shortage of reliable and consistent data on the interac-
tions between climate, environmental systems, and humans limits our ability to under-
stand and model how humans affect climate and vice versa.
Observational data sets contain inherent limitations such as measurement uncertain-
ties, gaps in spatial or temporal coverage, and the lack of continuity of calibrated re-
cords over extended periods of time (e.g., NRC, 2004; Trenberth et al., 2006). A prolifera-
tion of observational data sets, including retrospective reanalysis products and newly
available satellite measurements, has enhanced understanding of the climate system
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and its processes but has also led to a bewildering number of similar data sets. This
proliferation thus demands an assessment of the various observational products and
their usefulness for different purposes, as well as documentation of the differences
and uncertainties among the data sets. Users, including modelers, often do not have
sufficient information to appreciate the strengths and weaknesses of different data
products describing the same variable, and how they may be reliably used.
The high number of important climate variables suggests there is a need to prioritize
observational requirements within the climate observing system. Such prioritization
is fraught with difficulty, however, because of the underlying assumptions and the
fact that observations are used for multiple purposes. The Observing System Simula-
tion Experiment (OSSE) methodology can potentially be used to advance the rigor of
both climate model testing as well as climate observations (e.g., Norton et al., 2009).
Although model spatial resolution and errors currently limit the utility of OSSEs, OSSEs
will become more effective and powerful at prioritizing climate observations as mod-
els improve.
Although the existing collection of in situ observations covers most high-priority and
currently feasible measurements, their spatial and temporal coverage is incomplete,
and many improvements to the current climate observing system can be envisioned.
Such improvements would be based on technical innovations in measurement
techniques, the recognition of new needs for observations, and improved integration
of variables for societally relevant topics. There is also a general need for integration
and synthesis of satellite and in situ observations, which is partly met by reanalysis.
Observations from multiple sources are not necessarily redundant, because they can
complement each other and allow calibration and validation.
Some observation systems critical for model evaluation and improvement are at risk,
either because they require substantial investments that cannot be done incremen-
tally, or because budget constraints and aging equipment have gradually reduced
capabilities or data quality to unacceptable levels. While nations have continued to
recognize the importance of climate observations, for example through acceptance
of the Global Climate Observing System (GCOS) Implementation Plans, in many cases
funding commitments have not yet been made by GCOS member nations to provide
or improve key components of the climate observing system. The risk of major satel-
lite and in situ observing system holes is already present, and it may well grow in the
future. As discussed by Trenberth et al. (2011), there are many good aspects of the
current GCOS, but much remains to be done in order to provide the climate-quality
products required to develop and test the next generation of climate models and
Earth system models (ESMs). Process-oriented observations require further attention
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Integrated Climate Observing System and Earth System Analysis
and prioritization as well. These and other issues are explored in more detail in the fol-
lowing sections.
STATUS OF SYSTEMATIC CLIMATE OBSERVATION
A thorough summary of the organizational framework and status of systematic
climate observations, including by satellite, is provided by Trenberth et al. (2011). The
lead international organization for advisory oversight of systematic climate observa-
tions is GCOS.1 Its goal is to provide comprehensive information on the total climate
system, involving a multidisciplinary range of physical, chemical, and biological
properties, and atmospheric, oceanic, hydrologic, cryospheric, and terrestrial processes.
One of the most important roles of GCOS is to produce regular assessments of the
adequacy of climate observations, including suggestions for needed improvements.
Recent GCOS reports provide an excellent reference point for discussing the status of
climate observations.
Among other points, GCOS (2009) concluded that developed countries had improved
many of their climate observation capabilities, but there was little progress in ensur-
ing long-term continuity for several important observing systems and in filling gaps
in the in situ observing networks, with some evidence of decline. On the positive side,
GCOS (2009) concluded both operational and research networks and systems were
increasingly responsive to needs for climate data and information, including the need
for timely data exchange, and that space agencies had improved mission continuity
observational capability, data reprocessing, product generation, and access. Overall,
GCOS judged that the international climate observing system has progressed signifi-
cantly, but it still falls short of meeting all the climate information needs of the United
Nations Framework Convention on Climate Change (UNFCCC) and broader user
communities.
The Third World Climate Conference (WCC-3 in 2009) underscored the importance
of systematic observations (Karl et al., 2010; Manton et al., 2010) and recommended
strengthening GCOS in several ways. Of particular note were the WCC-3 recommen-
dations to sustain the established in situ and space-based components of GCOS;
enhance existing observing systems (e.g., fill gaps in spatial coverage, improve mea-
surement accuracy and frequency, and establish reference networks); apply the GCOS
1 http://www.wmo.int/pages/prog/gcos/ (accessed October 11, 2012).
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Climate Monitoring Principles (GCMPs2); improve the operation and planning of
observing systems; and rescue, exchange, archive, and catalog data, and recalibrate,
reprocess, and reanalyze long-term records, working toward full and unrestricted ac-
cess to data and products. High priority in WCC-3 was also given to the observational
needs for adaptation planning and to assisting developing countries to maintain and
strengthen their observing networks.
The 2010 update (GCOS, 2010) also noted advances in observational science and tech-
nology, an increasing focus on adaptation, and the demand to optimize mitigation
measures. It reaffirmed the importance of the GCMPs, emphasizing the need for con-
tinuity and stability of measurements. GCOS (2010) also provided a current listing of
“essential climate variables” (ECVs) (Table 5.1) and called for collocated measurement
of ecosystem variables along with the ECVs that influence or are influenced by them.
Finding 5.1: Observational networks and systems are increasingly responsive to
needs for climate data and information, but still fall short of meeting information
needs for climate and Earth system modeling.
CHALLENGES, GAPS, AND THREATS
Observational Needs for Earth System Models
The climate modeling enterprise differs from operational weather forecasting in
several significant ways. With respect to observations in support of modeling, one
important difference is the need for long-term, accurate measurements of a broad
range of Earth system components, including the oceans, land surface, biosphere,
and cryosphere as well as the atmosphere (GCOS, 2010, Table 1). The list of ECVs may
increase as climate models embrace increasingly more sophisticated treatments of
the Earth system (e.g., ice sheets, permafrost, land-surface hydrology, and the carbon
cycle). Because coupled climate models can drift if one component of the system is
poorly represented, subsystems that are poorly initialized, modeled, or constrained can
compromise the full climate solution. Moreover, feedbacks that are often controlled by
small-scale processes may cause such errors to grow and propagate in long-term (i.e.,
multidecadal) climate projections.
2 http://www.wmo.int/pages/prog/gcos/index.php?name=ClimateMonitoringPrinciples (accessed
October 11, 2012).
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Integrated Climate Observing System and Earth System Analysis
TABLE 5.1 Essential Climate Variables (ECVs) That Are Both Currently Feasible for
Global Implementation and Have a High Impact on UNFCCC Requirements (GCOS,
2010)
Domain Essential Climate Variables
Atmospheric Surface: Air temperature, wind speed and direction, water vapor, pressure, precipitation,
(over land, surface radiation budget.
sea, and ice) Upper-air: Temperature, wind speed and direction, water vapor, cloud properties, Earth
radiation budget (including solar irradiance).
Composition: Carbon dioxide, methane, and other long-lived greenhouse gases; ozone
and aerosol, supported by their precursors.
Oceanic Surface: Sea-surface temperature, sea-surface salinity, sea level, sea state, sea ice, surface
current, ocean color, carbon dioxide partial pressure, ocean acidity, phytoplankton.
Subsurface: Temperature, salinity, current, nutrients, carbon dioxide partial pressure,
ocean acidity, oxygen, tracers.
Terrestrial River discharge, water use, ground water, lakes, snow cover, glaciers and ice caps, ice
sheets, permafrost, albedo, land cover (including vegetation type), fraction of absorbed
photosynthetically active radiation, leaf area index, above-ground biomass, soil carbon,
fire disturbance, soil moisture.
For observational data sets to be most useful to climate model validation and verifica-
tion, or as boundary and initial conditions for modeling studies, most climate fields
need to be gridded and reasonably complete (i.e., without major geographic and tem-
poral gaps). The spatial density of required data depends on the application and on
the resolution of climate models in the coming decades (see below). As improved un-
derstanding and model representation of physical processes and feedbacks involved
in regional climate variability elevates in importance, more detailed and complete
regional observations will be required.
Specific examples of ECVs that are not routinely or globally available at present
include sea-ice thickness, deformation, drift and export, soil moisture, land carbon
stocks, the surface radiation budget, and stratospheric water vapor. Recommended
monitoring strategies for these and many other climate variables are provided in Karl
et al. (2010) and GCOS (2010). In other cases, climate variables may be well monitored
at present, but accuracy and continuity of the observations must be ensured, as well
as calibration and homogenization of data products that originate from different
platforms or instruments. All these data characteristics are essential to evaluation of
temporal trends, which provide some of the main “targets” for climate modeling.
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Process Studies
The number of physical processes included in climate and Earth system models is
increasing, and those occurring below the model grid scale are typically parameter-
ized. The observations needed to develop and calibrate the parameterization schemes
are most often obtained through intensive field campaigns of limited duration (e.g.,
months to a year or two). These campaigns are often referred to as process studies
(Cronin et al., 2009).
The timely transfer of information from process studies into climate and Earth system
models is critical, and within the United States this has been facilitated through mul-
tiagency funding of the Climate Process Team (CPT) concept. The key aim of a CPT is to
bridge gaps among field and remote sensing observation programs, process model-
ers, and global modelers by building new communities, in which those with observa-
tional expertise and data, those with highly detailed process models, and those build-
ing global models work together to address systematically the issues that most limit
progress in improving global climate models. The CPT concept has been successful in
supporting cross-institutional collaborations, an important concept because it is rare
to find single institutions with sufficient expertise in all of these areas. They are also
designed around “best practices” for process studies (Cronin et al., 2009), for example:
• modelers and observationalists should be integrated in the study from the
planning stage onward;
• integrated and synthesized data sets should be generated from the process
study observations to provide model-comparable data that can be used as
benchmarks for assessing and validating models; and
• broad use of the data should be encouraged through open data policies,
centralized access to all components of the experiment, and data archiving in
a user-friendly format.
Recent examples of process studies that followed these tenets include the U.S. CLIVAR
Variability of the American Monsoon Systems (VAMOS) Ocean-Cloud-Atmosphere-
Land Study Regional Experiment (VOCALS-REx), the Kuroshio Extension System Study,
the CLIVAR Mode Water Dynamic Experiment, and the North American Monsoon
Experiment. Web sites for each of these are given in Cronin et al. (2009).
The first round of pilot CPTs began in 2003 with National Science Foundation and
National Oceanic and Atmospheric Administration (NOAA) funding, and they resulted
in several significant achievements (U.S. CLIVAR Office, 2008). New ocean parameter-
izations were developed, for instance, for both mesoscale and submesoscale eddies
in the upper ocean in one CPT, while another produced new parameterizations for
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the shear-driven mixing in overflows, mixing in the frictional bottom layer, and repre-
sentations of dense water transport through ocean straights and down slopes. These
parameterizations were included in the ocean models at both the National Center for
Atmospheric Research (NCAR) and the Geophysical Fluid Dynamics Laboratory (GFDL),
and they would likely not have been developed without the CPT framework.
The principal legacies of the CPTs to date are the improved global models, but they
also initiated several new field experiments, trained early career scientists, and resulted
in a large number of peer-reviewed publications, including several synthesis and re-
view articles. Continued interaction between the team members from diverse fields is
another lasting, but perhaps less tangible, legacy.
The major challenge identified by the pilot CPTs was the manpower resources avail-
able at the national modeling centers. The full implementation and testing of highly
sophisticated parameterizations into coupled global models requires significant effort
extending beyond those supported by the CPT funds, which can be a difficult task
given competing demands such as Intergovernmental Panel on Climate Change (IPCC)
assessments. Also, in the absence of newly funded field campaigns, full integration of
observationalists can be a challenge.
Nevertheless, the CPT framework has proved effective, and a second CPT solicitation in
late 2009 is currently funding several new efforts. For a CPT to lead to model improve-
ments, several criteria need to be met (U.S. CLIVAR Office, 2008):
• Relevance: The process should be one that is currently poorly represented in
climate models, but where improvement in representation could lead to bet-
ter and more credible climate simulations.
• Readiness: The process should be one where recent theoretical developments,
process modeling, and observations are readily transferable into climate
models.
• Focus: The topic needs to be focused and well defined so as to lead to con-
crete results within the duration of the project.
• Model independence: The process should be of interest to developers of more
than one climate model.
There are many candidate processes to be considered by future CPTs, for example:
tropical convection, radiative transfer processes, aerosol indirect effects, cloud micro-
physics, land-surface processes including soil moisture and ice, ocean mesoscale eddy
processes, sea-ice processes, equatorial ocean upwelling and mixing, Southern Ocean
ventilation and deepwater formation, atmospheric gravity waves, air-sea fluxes, and
ice-sheet dynamics.
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Finding 5.2: By bridging gaps among field and remote sensing observation pro-
grams, process modelers, and global modelers, the Climate Process Team frame-
work has proven to be an effective mechanism to systematically address the
critical issues that limit progress in improving global climate models.
High-Resolution Models
High-resolution regional climate studies (ca. 1-10 km) are already common, and global
simulations at such resolutions will become commonplace over the next decade or
two. Some applications at these resolutions will be limited by the data that are avail-
able to initialize, calibrate, and evaluate models. For instance, improved observations
of precipitation frequency and intensity and of snow water equivalence in most of
the world’s mountain regions are needed to constrain high-resolution modeling of
precipitation patterns and snowline altitudes in complex topography (Nesbitt and
Anders, 2009). Details of sea-ice thickness and its variability are required to validate
model simulations of the dramatic changes being observed in polar regions (Vavrus et
al., 2012). Similar constraints apply to many aspects of coupled atmospheric and land-
surface models, such as flood forecasting (Booij, 2005; Dankers et al., 2007), estimation
of carbon fluxes due to thawing permafrost (Schuur et al., 2008), and quantification of
the climate impacts of land-use changes such as urbanization or deforestation.
Other applications may lend themselves to high-resolution regional atmosphere-
ocean modeling, but detailed data sets are needed to advance understanding of
processes involved as well as to provide accurate boundary conditions. One example
is modeling of ice-sheet mass balance in Greenland and Antarctica. Sea ice and open
water conditions affect heat and moisture advection to the ice sheets, affecting snow
accumulation and melt, so sea-ice concentration and coastal wind patterns need to be
well resolved and constrained, as do the larger-scale cyclonic systems that deliver heat
and moisture to the ice sheets. In addition, ocean mesoscale circulation patterns that
move warm water from depth into contact with sea ice, marine-based outlet glaciers,
and ice shelves play a leading role in interannual sea- and land-ice mass balance vari-
ability (e.g., Holland et al., 2008). Regional and coastal ocean models that simulate this
process require high-resolution boundary forcing from three-dimensional data sets for
ocean temperature, salinity, and currents as well as boundary-layer wind fields. Similar
constraints apply to simulation of nutrient and carbon fluxes in coastal ocean waters.
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Requirements for Sustained Data Collection and Synthesis
Data collection through satellite, airborne, radiosonde, ground-based, and marine-
based observing platforms needs to be sustained and, in some cases, enhanced. Much
of this can be leveraged off of the routine observations being done for weather and
marine forecasts, but climate modeling also has different needs. This includes decadal-
scale stability and continuity, the inclusion of some “slow-varying” parts of the climate
system (e.g., ice-sheet dynamics, subsurface ocean waters, forest/peatland carbon
stocks), and homogenization of data sets from different generations of instruments.
The latter includes changes in measurement standards and spatial/temporal sampling
density. The accuracy required for climate studies (e.g., 0.1 K for temperatures) requires
careful attention to data set homogenization.
There are numerous different climate reanalysis products, both within the United
States and globally (next section). Because these are continuous, gridded products,
they provide an essential “data set” for model calibration and validation over climatic
(multidecadal) time scales, for both the mean state and for temporal trends in dif-
ferent meteorological variables over the past ~60 years. One challenge for climate
model validation is to know which of the various reanalyzed data products is closest
to “truth”: that is, which product is most appropriate to evaluate a particular variable
for a particular part of the planet. There are significant discrepancies in the different
products (Trenberth et al., 2011) that need to be reconciled. In addition, there is a need
for more high-resolution or regional reanalysis products to validate high-resolution
models.
Similarly, there are multiple renditions of many variables, and the climate research
community needs to evaluate and synthesize these alternative data sets. A single or
limited number of recommended data sets that best represents each ECV would be
helpful for climate model validation and intercomparison exercises. One example
highlighting data set differences and the need for data assessment and intercompari-
son is the study of 20th-century sea-surface temperature (SST) trends (Deser et al.,
2010). Sea-surface temperature is a fundamental physical parameter of the climate
system and hence is a critical variable for models to simulate well. It is also well suited
for monitoring climate change due to the oceans’ large thermal inertia compared with
that of the atmosphere and land. Accurate determination of long-term SST trends
is hampered, however, by poor spatial and temporal sampling and inhomogeneous
measurement practices (Hurrell and Trenberth, 1999; Rayner et al., 2009). As a result,
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20th-century SST trends are subject to considerable uncertainty, limiting their physical
interpretation and utility as verification for climate model simulations. This uncertainty
is especially evident in the tropical Pacific where even the sign of the centennial trend
is in question (Vecchi et al., 2008). Similarly, Reynolds and Chelton (2010) show results
from six different SST products and highlight a number of significant differences
among them.
Ongoing improvements to measurement capability and resolution for a number of
climate fields will also facilitate improvements in the climate models. Many of these
innovations are recent, and the data being acquired create new opportunities for
climate modeling. For instance, sea-ice altimetry from ICESat, launched in 2003, pro-
vides the capability to estimate sea-ice thickness (Kwok and Rothrock, 2009), allowing
for more rigorous testing and calibration of sea-ice models. The Argo float network,
initially deployed in 2000 and now more than 3,300 strong, provides unprecedented
global-scale data of the upper 2,000 m of the ocean (e.g., Douglass and Knox, 2009).
Together with continuous and accurate top-of-the-atmosphere radiation measure-
ments, the Argo float network is critical to constraining climate models and under-
standing changes in the global heat budget. Satellite-based precipitation radar offers
the promise of exceptional spatial density and coverage (e.g., Nesbitt and Anders,
2009), an important supplement to ground-based precipitation networks. Such
observations need to be sustained for decades for climate applications; this requires
foresight and international cooperation, given the need for global coverage, the cost
of satellite missions, and the inevitability of occasional failures (e.g., ADEOS, Cryosat,
Glory).
Finding 5.3: To be useful for evaluating climate and Earth system models, ob-
servations need to be regionally comprehensive, global in scope, and interna-
tionally coordinated in a way that ensures consistency and transparency across
measurement standards, spatial and temporal sampling strategies, and data
management protocols (metadata standards, quality control, uncertainty esti-
mates, processing techniques, etc.).
Gaps and Threats
Long-term continuity of in situ and satellite-based observations is essential to provide
the data that are needed to advance climate science and to test, evaluate, and advance
models. Two of many examples are the satellite-based observations from Ice, Cloud,
and Land Elevation Satellite II (ICESat II) and Gravity Recovery and Climate Experiment
II (GRACE II). The former, scheduled for launch in 2016, will provide the continuous
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high-quality measurements of sea-ice and ice-sheet thickness essential to understand-
ing of interannnual variability versus decadal-scale trends in the cryosphere. Similar
urgency attends the continuity of satellite gravity measurements with the GRACE II
mission. Over the past several years GRACE data have provided important insights into
many features of the global climate, including the hydrologic cycle, sea-level rise, and
mass balance of the polar ice sheets. The prognostic capability of climate models and
ESMs hinges on the quality of such observational data sets and their ability to provide
insight into these and other essential Earth system processes.
The NRC Decadal Survey (NRC, 2007) reiterated the need to obtain “long-term, con-
tinuous, stable observations of the Earth system that are distinct from observations to
meet requirements … in support of numerical weather prediction.” It also articulated
a strategy for continuing and enhancing the U.S. Earth observing satellite system,
including recommended future missions to observe key processes in the Earth system
that would ultimately improve predictive capacity of both weather and climate events.
It is thus critical for the climate modeling community to have a coherent and active
voice in the planning of new space-based missions and instruments. Unfortunately,
however, the implementation of the Decadal Survey recommendations has been slow,
in part because of poor budgets but also because of launch failures and delays. In
the past 2 years, for instance, two climate satellites that would have provided critical
data on climate forcing (OCO and GLORY) crashed at launch. Further, NOAA has made
significant reductions in the scope of some future environmental satellite missions,
eliminating observational capabilities assumed by the Decadal Survey to be part of
NOAA’s future capability (NRC, 2012b).
Thus, despite some notable successes, the nation’s space-based observing capabil-
ity is in decline, with significantly fewer space-based observations than at any time in
recent decades. Earth observations face considerable challenges today (AMS, 2012),
and the continuity and stability of climate observations from satellites is thus seriously
threatened at just the time weather extremes are exceeding historical records. Overall,
the number of in-orbit and planned NASA and NOAA Earth observing missions will
decline by more than a factor of 3 by 2020, with a similar dramatic reduction in the
number of space-based Earth observing instruments (Figure 5.1; NRC, 2012b). Included
in this is also a looming gap in observations by polar orbiting satellites, for instance
between the expiration of NPP (National Polar-orbiting Operational Environmental
Satellite System [NPOESS] Preparatory Project; NPOESS was launched on October 28,
2011) and the launch of JPSS-1 (Joint Polar Satellite System; rescheduled for 2016). The
data gap could be six months with an optimistic estimate of the lifetime of NPP, but
could exceed two years if NPOESS lasts only 3 years. As discussed in GAO (2011), such
“a data gap would lead to less accurate and timely weather prediction models used
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FIGURE 5.1 The number of current and planned Earth observing missions and instruments from NOAA
and NASA showing a significant decline by 2020. Figure is courtesy of Stacey Boland, Jet Propulsion Lab
(personal communication).
to support weather forecasting, and advanced warning of extreme events—such as
hurricanes, storm surges, and floods—would be diminished,” potentially placing lives,
property, and critical infrastructure in greater danger.
Another issue with climate data from all sources is that there are significant differ-
ences in the metadata, availability, and provision of error and uncertainty estimates for
different climate data sets. Although it is difficult to make this globally conformable,
climate model validation and intercomparison exercises require a thorough under-
standing of the available data and their limitations. The climate observing and model-
ing communities are not optimally integrated, so observations are not always used
appropriately.
There needs to be more emphasis on detection and analysis of extreme weather in
both the observing and modeling communities, including hydrologic events (flood,
drought), severe storms (cyclones, tornadoes), snow and freezing rain events, and per-
sistent extreme temperatures (e.g., heat waves). These are the meteorological events
that impact society the most and, thus, are needed for informed decision making, but
the observing system and climate models themselves are ill equipped to capture and
simulate extreme conditions.
The timely availability of some climate observations may be at risk because of funding
shortfalls, data-sharing issues, gaps or unforeseen failures in current and future satel-
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lite missions, or transitions between systems (Sullivan, 2011; Zinser, 2011). Parties to
the UNFCCC approved the GCOS (2010) recommendations in principle, but funding
commitments at a national level are not in place in many cases. Budget cuts are erod-
ing the monitoring network in some GCOS member states.
Finding 5.4: Satellite-based observations are essential for the evaluation and ad-
vancement of climate and Earth system models. The U.S. space-based observing
system is now in peril, and the timely availability of some climate observations
may be at risk because of funding shortfalls, data-sharing issues, gaps or un-
foreseen failures in current and future satellite missions, or transitions between
systems.
ANALYSIS, ASSESSMENTS, AND REPROCESSING
Climate observations come from a diverse system of instruments and are spatially
and/or temporally incomplete (Figure 5.2). Meshing them with global climate models
to produce a best estimate of the state of the climate at a given point in time can en-
hance the value of diverse climate observations. The past decade has seen a prolifera-
tion of efforts to synthesize these diverse observations into a common framework to
produce global synoptic data sets for evaluating the atmospheric, oceanic, and terres-
trial components of climate and Earth system models. Such global analyses of climate
fields have supported many needs of the research and climate modeling communi-
ties. Because they are primarily produced by operational forecasting centers, which
are less concerned with long-term data consistency, many changes are made to both
the models and the assimilation systems over time. These changes produce spurious
“climate changes” in the analysis fields, which obscure the signals of true short-term
climate changes or interannual climate variability.
For the atmosphere a solution has been to redo the assimilation of the historical col-
lection of diverse atmospheric observations using a constant state-of-the-art numeri-
cal weather prediction model. These “reanalysis” efforts have produced fairly reliable
atmospheric climate records that have enabled (i) climatologies to be established, (ii)
anomalies to be calculated, (iii) empirical and quantitative diagnostic studies to be
conducted, (iv) exploration and improved understanding of climate system processes,
and (v) model initialization and validation to be performed (Trenberth, 2010). These
products provide the essential foundation for an accurate assessment of current
climate, diagnostic studies of features such as weather systems, monsoons, El Niño/
Southern Oscillation and other natural climate variations, seasonal prediction, and
climate predictability. Importantly, the reanalyses have also provided a vitally needed
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FIGURE 5.2 Climate observations come from a diverse system of instruments and are spatially and/or temporally incomplete. Meshing them
with global climate models to produce a best estimate of the state of the climate at a given point in time can enhance the value of diverse
climate observations. The top image shows atmospheric observations assimilated into GEOS-5 for a typical 6-hour assimilation window. The bot-
tom image shows the daily distribution of ocean observations throughout 1 month (September 2011). AIRS/IASI, Atmospheric Infrared Sounder/
Infrared Atmospheric Sounding Interferometer; AMV, Atmospheric Motion Vector; ATOVS, Advanced TIROS (Television Infrared Observation
Satellite) Operational Vertical Sounder; GPSRO, Global Positioning System Radio Occultation; TMI, TRMM (Tropical Rainfall measuring Mission)
Microwave Imager; XBT, expendable bathythermograph; TAO, Tropical Atmospheric-Ocean; PIRATA, Prediction and Research Moored Array in the
Atlantic; RAMA, Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction; CTD, conductivity temperature depth; SLA,
sea-level anomaly. SOURCE: Courtesy of Michele Rienecker, NASA.
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testbed for model improvement on all time scales, especially for seasonal-to-interan-
nual forecasts. Moreover, the basic assimilation and prediction systems are improved
as deficiencies are identified and corrected by applying them both in reanalysis and
routine weather and climate prediction. Besides improvement in the assimilating
model and much better resolution, the data sets that have been analyzed have also
evolved. Nonetheless, a serious problem is effects of changes in the observing system
that produces spurious changes in the perceived climate. As a result, estimates of
trends and low-frequency variability have been unreliable, and this problem is exacer-
bated by model bias.
Analysis and reanalysis are being extended to support research on other aspects of
the climate system too. Data assimilation efforts have grown in the United States, for
instance, and now include assimilation of data for weather (e.g., National Centers for
Environmental Prediction), seasonal-to-interannual climate variability (e.g., Climate
Prediction Center), satellite data (e.g., GMAO MERRA), ocean circulation (e.g., GODAE),
and land surface (e.g., GLDAS). Moreover, as assimilation techniques for observa-
tions of atmospheric trace constituents (e.g., aerosols, ozone, and carbon dioxide) are
refined, reanalysis should eventually provide the means to develop consistent clima-
tologies for the chemical components of the atmosphere, including the carbon cycle,
and thus help to quantify key uncertainties in the radiative forcing of climate (IPCC,
2007c). Analysis of ocean data has led to novel data products based on the historical
ocean data, so that there are now about 20 different analyses of ocean temperatures
and ocean heat content (see Lyman et al., 2010; Palmer et al., 2011). However, there
are large discrepancies among them, similar to the many atmospheric analysis and
reanalysis products.
Thus, as well as assessments of data sets of individual variables, assessments of re-
analyses are also essential, especially with the recent proliferation of atmospheric and
ocean reanalysis data sets. Many are created for specific purposes but all differ, often
substantially, and the strengths and weaknesses or assumptions are currently neither
well understood nor documented. Consequently, assessments are required to evaluate
these aspects and help improve the data sets. Moreover, continuous reprocessing is
essential. Reprocessing can account for recalibration of satellite data, take advantage
of new knowledge and algorithms, and rectify problems and errors that have become
evident. As stated by Trenberth et al. (2011), “repeat reprocessing and assessment
should be hall marks of a climate observing system.”
Finally, promising developments are occurring in sea-ice and land-surface reanaly-
sis, and coupled data assimilation systems are beginning to be developed. Coupled
analysis and reanalysis products are necessary to provide the physically consistent
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initial conditions for developing decadal prediction systems, which have the potential
to advance adaptation and mitigation planning. Improvements in reanalysis depend
on continued support for the underpinning research and required observations, the
development of comprehensive Earth system models to expand the scope of reanaly-
sis, and the infrastructure for data handling and processing.
Finding 5.5: Assessments of data sets, of individual variables, and of reanalyses
are essential to ensure quality data for the evaluation and development of cli-
mate models.
THE WAY FORWARD
Earth is observed more extensively today than at any other time, but many of the
observations are not of sufficient quality to monitor long-term climate variability and
change. Moreover, some observation systems critical for process-level understanding
and model evaluation and improvement are at risk, with declines in both quality and
coverage. Gaps also exist in important Earth system observing systems, both in terms
of existing systems and new types of observations necessary for improving our capac-
ity to predict future changes in climate especially on regional scales. The U.S. space-
based observing system is now in peril, with an anticipated 75 percent reduction in
the number of NOAA and NASA missions over the next decade, and an associated
reduction in the number of observing instruments from approximately 90 today to 20
or so by the end of the current decade.
The committee thus strongly supports the findings and recommendations from a
number of previous relevant National Research Council reports on the status of the
climate observing systems and the importance of reanalysis efforts:
• NRC (2009): “A U.S. climate observing system … should be established to
ensure that data needed to address climate change are collected or continued.
[This includes] augmenting current satellite and ground observing systems
… and support [for] new types of observations, including human dimen-
sions observations that are needed for developing mitigation and adaptation
strategies.”
• NRC (2009): “[E]xpand and maintain national observation systems to … fill
critical gaps [and support] modeling and process studies.”
• NRC (2010b): “Redouble efforts to develop, deploy, and maintain a compre-
hensive climate observing system that can support all aspects of understand-
ing and responding to climate change.”
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• NRC (2009): “[The United States] should sustain production of atmosphere
and ocean reanalyses, further develop and support research on coupled data
assimilation techniques … , and improve coordination with similar efforts in
other countries.”
In addition, several major recommendations have emerged from this report. First,
the diverse suite of climate observations should continue to be scrutinized in order
to diagnose the state of the changing climate and understanding the evolving dy-
namics of the system. Both confrontation of climate model simulations with climate
observations and enhanced communication between the modeling and observa-
tional communities are critical for assessing model performance, for improving the
representation of physical processes in the models, and in some cases for identifying
problems with observational data sets. The assimilation of observations into models
exploits known relationships among the different climate variables to select or reject
the observations and to propagate and/or extrapolate the observations into data gaps
in space or time. Data assimilation efforts in the United States and elsewhere, however,
operate independently and use separate models. They therefore have not taken full
advantage of the entire suite of observations for the Earth system.
A way to rectify this situation would be the establishment of a national Earth system
data assimilation effort that simultaneously merges weather observations, satellite
radiances or retrievals for precipitation and various trace constituents, ocean mea-
surements, and land and other observations into a full Earth system model, such as
one used for climate projections, so as to make full use of the coupled and interactive
nature of the Earth system to constrain the data analysis products.
Hand in hand with the Earth system data assimilation effort is the renewed and con-
tinued analysis of the available observations, especially in terms of climate variability
at regional scales. Regional-scale climate variability is inherently greater than large-
scale variability, and many aspects of it are poorly simulated in the current genera-
tion of global climate models. Furthermore, the causes and signatures of decadal
and multidecadal variability need to be extracted from the observations and used to
assess climate model simulations on these scales. The committee believes that the
nation should continue to sustain its effort in the analysis and comparison of different
data sets, including reanalysis products, to improve documentation of their strengths,
weaknesses, uncertainties, and utility for different purposes, including model develop-
ment and evaluation, as well as renew its effort in the analysis of available observa-
tions, especially in terms of the nature and causes of climate variability at regional
scales. One effort in this direction is the web-based informed guide to selected climate
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data sets of relevance to the evaluation of Earth system models, available from NCAR.3
The two main objectives of this work are to (1) evaluate and assess often-used climate
data sets and (2) provide “expert-user” guidance and advice on the strengths and
limitations of selected observational data sets and their applicability to model evalu-
ations. Another effort in its early stages is “Obs4MIPs,” which is an attempt to provide
modeling groups with a limited collection of well-established and documented data
sets that have been organized according to the CMIP5 model output requirements.4
More activities along these lines should be supported, because they are vital to the
integrity of observational, modeling, and prediction studies of climate variability and
change.
Climate data archives are scattered among federal agencies, laboratories, universi-
ties, and other repositories (also discussed in Chapter 10). While data catalogs exist,
it is not easy for the scientific investigator or the decision maker to access and/or
download the multidisciplinary data sets in various formats, subset them, “regularize”
them (put them onto common grids, time spacing, units, etc.), and analyze them to
advance understanding of the Earth system. The advances of information technology
(e.g., OpenDAP3, Goddard Giovanni4) have enabled the remote analysis of subsets of
the climate data. These information technology (IT) advances need to be brought to
bear on the entire climate data holding, linking all the data repositories (regardless of
agency) with a user-friendly nonexpert interface to the data that makes it easy and
fast to find variables. This interface would support the ability for interactive standard
analyses of the data sets and the download of subsets of the data and the analysis
results. The formatting and gridding of the various data sets should not be an issue to
the user. Such a national IT infrastructure for Earth system data could facilitate and ac-
celerate advances in data display, visualization, and analysis and could be regarded as
a natural philosophical extension of the community software infrastructure proposed
in Chapter 10. Ideally, the development of such an infrastructure would be primar-
ily community organized and well coordinated with model intercomparison efforts
(which require exactly this kind of product, but then also generate model outputs on
the same grid). It would be useful if an entity that has the ability to coordinate the
efforts of multiple agencies, laboratories, and universities were to endorse this effort
and help achieve an interagency agreement for how to support it. While other orga-
nizations could perhaps fill this coordinating role, the U.S. Global Change Research
Program (Box 2.1) is the most obvious possibility.
3 http://climatedataguide.ucar.edu (accessed October 11, 2012).
4 http://obs4mips.llnl.gov:8080/wiki/ (accessed October 11, 2012).
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Recommendation 5.1: The committee reiterates the statements of previous
reports that call on the United States to continue and to augment the support for
Earth observations and to address the potential for serious gaps in the space-
based observation system. A particular priority should be maintaining funda-
mental climate-quality observational data sets that have been gathered for 20
years or longer.
Recommendation 5.2: To better synthesize the diversity of climate-relevant
observations, the United States should establish a national Earth system data
assimilation effort that builds from existing efforts and merges weather observa-
tions, satellite radiances or retrievals for precipitation and various trace constitu-
ents, ocean measurements, and land and other observations into the same Earth
system model simultaneously.
Recommendation 5.3: Building from existing efforts, the United States should
develop a national IT infrastructure for Earth system data so as to facilitate and
accelerate data display, visualization, and analysis.
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