Climate Science for Serving Society: Research, Modeling and Prediction Priorities

506 Pages·2013·12.713 MB·English

by  Ghassem R. Asrar, James W. Hurrell (auth.), Ghassem R. Asrar, James W. Hurrell (eds.)

Ghassem R. Asrar
James W. Hurrell
Editors
Climate Science for
Serving Society
Research, Modeling and
Prediction Priorities
Climate Science for Serving Society
Decadal -mean, ensemble-average of near-surface air temperature (°C) and sea ice extent (%)
anomalies simulated for historical and future conditions by the Community Climate System Model,
version 4 (CCSM4 ). Future conditions are projected using the Representative Concentration
Pathway 8.5 (RCP8.5) emission scenario. Anomalies are relative to 1850-1899 conditions, as
simulated by six-member ensembles of CCSM4. The CCSM4 is one of more than 20 climate and
Earth system models contributing to the Coupled Model Intercomparison Project Phase 5 (CMIP5)
that was established by the World Climate Research Programme, through its Working Group on
Coupled Modeling, as a standard experimental protocol for studying the output of coupled climate
models. It provides a community-based infrastructure in support of climate model diagnosis,
validation, documentation and data access, thus enabling a diverse community of scientists to
analyze climate model output in a systematic fashion. Virtually, the entire international climate
modeling community has participated in the CMIP project since its inception in 1995.
Ghassem R. Asrar (cid:129) James W. Hurrell

Editors
Climate Science
for Serving Society
Research, Modeling and Prediction Priorities
Editors
Ghassem R. Asrar James W. Hurrell
World Climate Research Programme Earth System Laboratory
World Meteorological Organization National Center for Atmospheric Research
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The cover image was developed for the World Climate Research Program (WCRP) Open
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benefi ts of science-based climate information for decision-makers and citizens worldwide in
addressing the opportunities and challenges associated with climate variability and changes.
Chapter 17: © World Health Organization 2013.
All rights reserved. The World Health Organization has granted the Publisher permission for
the reproduction of this chapter.
The author (Stéphane Alexandre Louis Hugonnet) is a staff member of the World Health
Organization. The author alone is responsible for the views expressed in this publication and
they do not necessarily represent the decisions or policies of the World Health Organization.
ISBN 978-94-007-6691-4 ISBN 978-94-007-6692-1 (eBook)
DOI 10.1007/978-94-007-6692-1
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Contents
The World Climate Research Program Strategy
and Priorities: Next Decade ........................................................................... 1
Ghassem R. Asrar, James W. Hurrell, and Antonio J. Busalacchi
Challenges of a Sustained Climate Observing System ................................ 13
Kevin E. Trenberth, Richard A. Anthes, Alan Belward, Otis B. Brown,
Ted Habermann, Thomas R. Karl, Steve Running, Barbara Ryan,
Michael Tanner, and Bruce Wielicki
On the Reprocessing and Reanalysis of Observations for Climate ............ 51
Michael G. Bosilovich, John Kennedy, Dick Dee, Rob Allan,
and Alan O’Neill
Climate Processes: Clouds, Aerosols and Dynamics .................................... 73
Steven C. Sherwood, M. Joan Alexander, Andy R. Brown,
Norm A. McFarlane, Edwin P. Gerber, Graham Feingold,
Adam A. Scaife, and Wojciech W. Grabowski
Aerosol Cloud-Mediated Radiative Forcing: Highly Uncertain
and Opposite Effects from Shallow and Deep Clouds ................................. 105
Daniel Rosenfeld, Robert Wood, Leo J. Donner, and Steven C. Sherwood
Improving Understanding of the Global Hydrologic Cycle
Observation and Analysis of the Climate System:
The Global Water Cycle ................................................................................. 151
Peter H. Gleick, Heather Cooley, James S. Famiglietti, Dennis P. Lettenmaier,
Taikan Oki, Charles J. Vörösmarty, and Eric F. Wood
Land Use and Land Cover Changes and Their Impacts
on Hydroclimate, Ecosystems and Society .................................................... 185
Taikan Oki, Eleanor M. Blyth, Ernesto Hugo Berbery,
and Domingo Alcaraz-Segura
v
vi Contents
Prediction from Weeks to Decades ................................................................ 205
Ben Kirtman, David Anderson, Gilbert Brunet, In-Sik Kang,
Adam A. Scaife, and Doug Smith
Assessing the Reliability of Climate Models, CMIP5 .................................. 237
Bart van den Hurk, Pascale Braconnot, Veronika Eyring,
Pierre Friedlingstein, Peter Gleckler, Reto Knutti, and Joao Teixeira
Changes in Variability Associated with Climate Change ............................ 249
Karen H. Rosenlof, Laurent Terray, Clara Deser, Amy Clement,
Hugues Goosse, and Sean Davis
Understanding and Predicting Climate Variability and Change
at Monsoon Regions ........................................................................................ 273
Carolina Vera, William Gutowski, Carlos R. Mechoso, B.N. Goswami,
Chris C. Reason, Chris D. Thorncroft, Jose Antonio Marengo,
Bruce Hewitson, Harry Hendon, Colin Jones, and Piero Lionello
Attribution of Weather and Climate-Related Events .................................. 307
Peter A. Stott, Myles Allen, Nikolaos Christidis, Randall M. Dole,
Martin Hoerling, Chris Huntingford, Pardeep Pall, Judith Perlwitz,
and Dáithí Stone
Climate Extremes: Challenges in Estimating and Understanding
Recent Changes in the Frequency and Intensity of Extreme
Climate and Weather Events.......................................................................... 339
Francis W. Zwiers, Lisa V. Alexander, Gabriele C. Hegerl,
Thomas R. Knutson, James P. Kossin, Phillippe Naveau, Neville Nicholls,
Christoph Schär, Sonia I. Seneviratne, and Xuebin Zhang
Carbon Dioxide and Climate: Perspectives
on a Scientifi c Assessment .............................................................................. 391
Sandrine Bony, Bjorn Stevens, Isaac H. Held, John F. Mitchell,
Jean- Louis Dufresne, Kerry A. Emanuel, Pierre Friedlingstein,
Stephen Griffi es, and Catherine Senior
Atmospheric Composition, Irreversible Climate Change,
and Mitigation Policy ...................................................................................... 415
Susan Solomon, Raymond T. Pierrehumbert, Damon Matthews,
John S. Daniel, and Pierre Friedlingstein
Building Adaptive Capacity to Climate Change in Less
Developed Countries ....................................................................................... 437
Maria Carmen Lemos, Arun Agrawal, Hallie Eakin, Don R. Nelson,
Nathan L. Engle, and Owen Johns
A Climate and Health Partnership to Inform the Prevention
and Control of Meningoccocal Meningitis in Sub-Saharan
Africa: The MERIT Initiative ........................................................................ 459
Madeleine C. Thomson, E. Firth, M. Jancloes, A. Mihretie, M. Onoda,
S. Nickovic, H. Broutin, S. Sow, W. Perea, E. Bertherat, and S. Hugonnet
List of Figures
Chapter 1
Fig. 1 A conceptual illustration of how the fi ve major pillars
of the Global Framework for Climate Services (GFCS) must
function in a coordinated and integrated manner to realize
the GFCS grand vision in near-, mid- and long-term
(Adapted from WMO 2011) ................................................................ 6
Chapter 2
Fig. 1 Changes in the mix and increasing diversity of observations
over time create challenges for a consistent climate record
(Courtesy, S. Brönnimann, University of Bern.
Adapted from Brönnimann et al. 2008) .............................................. 16
Fig. 2 Relationship of extreme phenomena to ECVs for monitoring.
Both the phenomena and the ECVs are color coded to describe
the adequacy of the current monitoring systems to capture trends
on climate timescales set against alternating grey and white lines
to enhance readability. Green indicates global coverage with
a suffi cient period of record, data quality, and metadata to make
enable meaningful monitoring of temporal changes. Yellow
indicates an insuffi ciency in one of those three factors. Red
indicates insuffi ciency in more than one of the factors.
The check mark in the colored ECV block indicates that
the ECV is of primary importance to monitoring changes
in the extreme event phenomenon ....................................................... 21
Fig. 3 Key components of an operational climate capability. Here GSICS
is the global space- based intercalibration system, IGDDS WMO
integrated global data dissemination service, SCOPE-CM
sustained coordinated processing of environmental satellite
data for climate monitoring, VLab virtual laboratory
for training in satellite meteorology ..................................................... 22
vii
viii List of Figures
Fig. 4 Schematic of the space-based global observing system
(GOS) as of about 2010 ..................................................................... 22
Fig. 5 The schematic shows the role of climate process
and monitoring observations in climate change science:
detection and attribution of climate change, climate
model testing, and climate model improvements .............................. 27
Fig. 6 Traceability of uncertainty in decadal change observations
between two decades of data, followed by comparison
of the observed decadal change with climate model predicted
change. While the entire chain of uncertainty must be
characterized, even perfect observations are limited by noise
from natural variability of Earth’s climate system itself
(e.g., ENSO) when used to test climate models. The goal
is to drive observation uncertainties to roughly a factor
of 2 less than natural variability ........................................................ 30
Fig. 7 Time series of the mean and standard deviations of the
ECMWF background and analysis temperatures at 100 hPa
showing a reduction in the bias errors on 12 December 2006
(green arrow) when COSMIC data began to be assimilated
(After Luntama et al. 2008; courtesy Anthes 2011) .......................... 31
Fig. 8 Monthly zonal FAPAR anomalies relative to the period
January 1998 to December 2010 estimated from decadal
FAPAR products derived at a resolution of 0.5 × 0.5° from
measurements acquired by the SeaWiFS (NASA) and MERIS
(ESA) sensors. As rates of photosynthesis are affected by
temperature and precipitation, FAPAR is an indicator of climate
impacts on vegetation; favorable temperatures and soil moisture
availability are accompanied by higher than average FAPAR
values, drought and/or excessive temperature are accompanied
by lower values (Gobron et al. 2010) ................................................ 33
Fig. 9 The components of the global fl ow of energy through
the climate system as given by Trenberth et al. (2009)
as background values are compared with values from
eight different reanalyses for 2002–2008 (except ERA-40
is for the 1990s), as given at lower left in the Figure,
in W m−2. From Trenberth et al. (2011). For example,
the estimated imbalance at TOA and at the surface
is 0.9 W m−2 for the 2002–2008 period, or 0.6 W m−2
for the 1990s, but values from reanalyses differ
substantially at TOA and at the surface, and also differ
between the two values implying a large source or sink
in the atmosphere. Differences reveal assimilating
model biases and the effects of analysis increments ......................... 37
Fig. 10 Observations of the ten indicators over time (SOC 2009)
(Adapted from fi gure courtesy NCDC, NOAA) ............................... 41
Fig. 11 Estimated number of NASA/NOAA Earth Observing
instruments in space out to 2020 (NRC 2012) .................................. 43
List of Figures ix
Chapter 3
Fig. 1 Four examples showing that very different behaviors are
consistent with the same ‘error bars’. (Top) uncertainty
range indicates that high-frequency variability is missing.
(second from top) uncertainty range indicates a systematic
offset. (bottom and second from bottom) uncertainty range
indicates red-noise error variance ..................................................... 58
Chapter 5
Fig. 1 Radiative forcing estimates of atmospheric compounds from
the pre-industrial period 1750–2007 (From Isaksen et al. 2009)
in W/m2 ............................................................................................. 109
Fig. 2 The dependence of drizzling regimes in marine stratocumulus
clouds on drop number concentration and cloud depth. Heavy
drizzle is defi ned where most water resides in the drizzle drops.
Light drizzle is defi ned where most water resides in the cloud
drops. The cloud drop effective radius of r = 16 μm was shown
e
to be the minimal size for the heavy drizzle regime (Gerber
1996). Transition to light drizzle occurs between r of 14–16 μm.
e
The dashed line separates between negligible drizzle and
light drizzle of R > 0.2 mm day−1 is based on DYCOMS-II
observations. The red lines show the approximation of R ~ h3/N,
d
for R of 0.1, 0.5 and 1 mm/day. The individual points and their
R values are posted (From Table 3 of van Zanten et al. 2005.
After Rosenfeld et al. (ACP 2006a)) ................................................. 112
Fig. 3 A schematic illustration of the mechanism for transition from
non precipitating closed Benard cells to precipitating open cells
and onward to nearly complete rainout and elimination of the
clouds (After Rosenfeld et al. 2006a). In the closed cells
(a) the convection is propelled by thermal radiative cooling
from the tops of the extensive deck of clouds with small drops.
The clouds mix aerosols and vapor with the free troposphere
from above. The onset of drizzle depletes the water from the cloud
deck and cools the sub-cloud layer (b). This leads to decoupling
of the cloud cover and to its subsequent breaking. The downdrafts
due to the evaporational cooling starts triggering new convection
(c). The propulsion of the convection undergoes transition
from radiative cooling at the top of the fully cloudy MBL to surface
heating at the bottom of the partly cloudy MBL. This causes a
reversal of the convection from closed to open Benard cells,
that develop, rainout and produce downdrafts that trigger
new generations of such rain cells (d). The mixing with of aerosols
with the free troposphere at cloud tops is much reduced. Therefore,
the process can continue to a runaway effect of cleansing
by the CCN and direct condensation into drizzle that directly
precipitates and prevents the cloud formation altogether (e).
The satellite strip is a 300 km long excerpt from Fig. 4 .................... 115