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Steven C. Hardiman
,
Ian A. Boutle
,
Andrew C. Bushell
,
Neal Butchart
,
Mike J. P. Cullen
,
Paul R. Field
,
Kalli Furtado
,
James C. Manners
,
Sean F. Milton
,
Cyril Morcrette
,
Fiona M. O’Connor
,
Ben J. Shipway
,
Chris Smith
,
David N. Walters
,
Martin R. Willett
,
Keith D. Williams
,
Nigel Wood
,
N. Luke Abraham
,
James Keeble
,
Amanda C. Maycock
,
John Thuburn
, and
Matthew T. Woodhouse

Abstract

A warm bias in tropical tropopause temperature is found in the Met Office Unified Model (MetUM), in common with most models from phase 5 of CMIP (CMIP5). Key dynamical, microphysical, and radiative processes influencing the tropical tropopause temperature and lower-stratospheric water vapor concentrations in climate models are investigated using the MetUM. A series of sensitivity experiments are run to separate the effects of vertical advection, ice optical and microphysical properties, convection, cirrus clouds, and atmospheric composition on simulated tropopause temperature and lower-stratospheric water vapor concentrations in the tropics. The numerical accuracy of the vertical advection, determined in the MetUM by the choice of interpolation and conservation schemes used, is found to be particularly important. Microphysical and radiative processes are found to influence stratospheric water vapor both through modifying the tropical tropopause temperature and through modifying upper-tropospheric water vapor concentrations, allowing more water vapor to be advected into the stratosphere. The representation of any of the processes discussed can act to significantly reduce biases in tropical tropopause temperature and stratospheric water vapor in a physical way, thereby improving climate simulations.

Full access
L. C. Slivinski
,
G. P. Compo
,
P. D. Sardeshmukh
,
J. S. Whitaker
,
C. McColl
,
R. J. Allan
,
P. Brohan
,
X. Yin
,
C. A. Smith
,
L. J. Spencer
,
R. S. Vose
,
M. Rohrer
,
R. P. Conroy
,
D. C. Schuster
,
J. J. Kennedy
,
L. Ashcroft
,
S. Brönnimann
,
M. Brunet
,
D. Camuffo
,
R. Cornes
,
T. A. Cram
,
F. Domínguez-Castro
,
J. E. Freeman
,
J. Gergis
,
E. Hawkins
,
P. D. Jones
,
H. Kubota
,
T. C. Lee
,
A. M. Lorrey
,
J. Luterbacher
,
C. J. Mock
,
R. K. Przybylak
,
C. Pudmenzky
,
V. C. Slonosky
,
B. Tinz
,
B. Trewin
,
X. L. Wang
,
C. Wilkinson
,
K. Wood
, and
P. Wyszyński

Abstract

The performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the twentieth century. Upper-air fields from 20CRv3 in the late twentieth century and early twenty-first century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for 1979–2015 is comparable to that of modern operational 3–4-day forecasts. Finally, 20CRv3 performs well on climate time scales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric-layer temperatures that correlate well with independent products in the twentieth century, placing recent trends in a longer historical context.

Open access
M. Rodell
,
H. K. Beaudoing
,
T. S. L’Ecuyer
,
W. S. Olson
,
J. S. Famiglietti
,
P. R. Houser
,
R. Adler
,
M. G. Bosilovich
,
C. A. Clayson
,
D. Chambers
,
E. Clark
,
E. J. Fetzer
,
X. Gao
,
G. Gu
,
K. Hilburn
,
G. J. Huffman
,
D. P. Lettenmaier
,
W. T. Liu
,
F. R. Robertson
,
C. A. Schlosser
,
J. Sheffield
, and
E. F. Wood

Abstract

This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.

Full access
H. G. Hidalgo
,
T. Das
,
M. D. Dettinger
,
D. R. Cayan
,
D. W. Pierce
,
T. P. Barnett
,
G. Bala
,
A. Mirin
,
A. W. Wood
,
C. Bonfils
,
B. D. Santer
, and
T. Nozawa

Abstract

This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.

Full access
P.B. Russell
,
M.P. McCormick
,
T.J. Swissler
,
W.P. Chu
,
J.M. Livingston
,
W.H. Fuller
,
J.M. Rosen
,
D.J. Hofmann
,
L.R. McMaster
,
D.C. Woods
, and
T.J. Pepin

Abstract

We show results from the first set of measurements conducted to validate extinction data from the satellite sensor SAM II. Dustsonde-measured number density profiles and lidar-measured backscattering profiles for two days are converted to extinction profiles using the optical modeling techniques described in the companion Paper I (Russell et al., 1981). At heights ∼2 km and more above the tropopause, the dustsonde data are used to restrict the range of model size distributions, thus reducing uncertainties in the conversion process. At all heights, measurement uncertainties for each sensor are evaluated, and these are combined with conversion uncertainties to yield the total uncertainty in derived data profiles.

The SAM II measured, dustsonde-inferred, and lidar-inferred extinction profiles for both days are shown to agree within their respective uncertainties at all heights above the tropopause. Near the tropopause, this agreement depends on the use of model size distributions with more relatively large particles (radius ≳0.6 μm) than are present in distributions used to model the main stratospheric aerosol peak. The presence of these relatively large particles is supported by measurements made elsewhere and is suggested by in situ size distribution measurements reported here. These relatively large particles near the tropopause are likely to have an important bearing on the radiative impact of the total stratospheric aerosol.

The agreement in this experiment supports the validity of the SAM II extinction data and the SAM II uncertainty estimates derived from an independent error analysis. Recommendations are given for reducing the uncertainties of future correlative experiments.

Full access
Tristan S. L’Ecuyer
,
H. K. Beaudoing
,
M. Rodell
,
W. Olson
,
B. Lin
,
S. Kato
,
C. A. Clayson
,
E. Wood
,
J. Sheffield
,
R. Adler
,
G. Huffman
,
M. Bosilovich
,
G. Gu
,
F. Robertson
,
P. R. Houser
,
D. Chambers
,
J. S. Famiglietti
,
E. Fetzer
,
W. T. Liu
,
X. Gao
,
C. A. Schlosser
,
E. Clark
,
D. P. Lettenmaier
, and
K. Hilburn

Abstract

New objectively balanced observation-based reconstructions of global and continental energy budgets and their seasonal variability are presented that span the golden decade of Earth-observing satellites at the start of the twenty-first century. In the absence of balance constraints, various combinations of modern flux datasets reveal that current estimates of net radiation into Earth’s surface exceed corresponding turbulent heat fluxes by 13–24 W m−2. The largest imbalances occur over oceanic regions where the component algorithms operate independent of closure constraints. Recent uncertainty assessments suggest that these imbalances fall within anticipated error bounds for each dataset, but the systematic nature of required adjustments across different regions confirm the existence of biases in the component fluxes. To reintroduce energy and water cycle closure information lost in the development of independent flux datasets, a variational method is introduced that explicitly accounts for the relative accuracies in all component fluxes. Applying the technique to a 10-yr record of satellite observations yields new energy budget estimates that simultaneously satisfy all energy and water cycle balance constraints. Globally, 180 W m−2 of atmospheric longwave cooling is balanced by 74 W m−2 of shortwave absorption and 106 W m−2 of latent and sensible heat release. At the surface, 106 W m−2 of downwelling radiation is balanced by turbulent heat transfer to within a residual heat flux into the oceans of 0.45 W m−2, consistent with recent observations of changes in ocean heat content. Annual mean energy budgets and their seasonal cycles for each of seven continents and nine ocean basins are also presented.

Full access
P. W. Thorne
,
R. J. Allan
,
L. Ashcroft
,
P. Brohan
,
R. J. H Dunn
,
M. J. Menne
,
P. R. Pearce
,
J. Picas
,
K. M. Willett
,
M. Benoy
,
S. Bronnimann
,
P. O. Canziani
,
J. Coll
,
R. Crouthamel
,
G. P. Compo
,
D. Cuppett
,
M. Curley
,
C. Duffy
,
I. Gillespie
,
J. Guijarro
,
S. Jourdain
,
E. C. Kent
,
H. Kubota
,
T. P. Legg
,
Q. Li
,
J. Matsumoto
,
C. Murphy
,
N. A. Rayner
,
J. J. Rennie
,
E. Rustemeier
,
L. C. Slivinski
,
V. Slonosky
,
A. Squintu
,
B. Tinz
,
M. A. Valente
,
S. Walsh
,
X. L. Wang
,
N. Westcott
,
K. Wood
,
S. D. Woodruff
, and
S. J. Worley

Abstract

Observations are the foundation for understanding the climate system. Yet, currently available land meteorological data are highly fractured into various global, regional, and national holdings for different variables and time scales, from a variety of sources, and in a mixture of formats. Added to this, many data are still inaccessible for analysis and usage. To meet modern scientific and societal demands as well as emerging needs such as the provision of climate services, it is essential that we improve the management and curation of available land-based meteorological holdings. We need a comprehensive global set of data holdings, of known provenance, that is truly integrated both across essential climate variables (ECVs) and across time scales to meet the broad range of stakeholder needs. These holdings must be easily discoverable, made available in accessible formats, and backed up by multitiered user support. The present paper provides a high-level overview, based upon broad community input, of the steps that are required to bring about this integration. The significant challenge is to find a sustained means to realize this vision. This requires a long-term international program. The database that results will transform our collective ability to provide societally relevant research, analysis, and predictions in many weather- and climate-related application areas across much of the globe.

Open access
T. H. Chen
,
A. Henderson-Sellers
,
P. C. D. Milly
,
A. J. Pitman
,
A. C. M. Beljaars
,
J. Polcher
,
F. Abramopoulos
,
A. Boone
,
S. Chang
,
F. Chen
,
Y. Dai
,
C. E. Desborough
,
R. E. Dickinson
,
L. Dümenil
,
M. Ek
,
J. R. Garratt
,
N. Gedney
,
Y. M. Gusev
,
J. Kim
,
R. Koster
,
E. A. Kowalczyk
,
K. Laval
,
J. Lean
,
D. Lettenmaier
,
X. Liang
,
J.-F. Mahfouf
,
H.-T. Mengelkamp
,
K. Mitchell
,
O. N. Nasonova
,
J. Noilhan
,
A. Robock
,
C. Rosenzweig
,
J. Schaake
,
C. A. Schlosser
,
J.-P. Schulz
,
Y. Shao
,
A. B. Shmakin
,
D. L. Verseghy
,
P. Wetzel
,
E. F. Wood
,
Y. Xue
,
Z.-L. Yang
, and
Q. Zeng

Abstract

In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m−2 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (±10 W m−2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models’ neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of30 W m−2 and 25 W m−2, respectively. Annual totals of evapotranspiration and runoff, into which the precipitation is partitioned, both have ranges of 315 mm. These ranges in annual heat and water fluxes were approximately halved upon exclusion of the three schemes that have no stomatal resistance under non-water-stressed conditions. Many schemes tend to underestimate latent heat flux and overestimate sensible heat flux in summer, with a reverse tendency in winter. For six schemes, root-mean-square deviations of predictions from monthly observations are less than the estimated upper bounds on observation errors (5 W m−2 for sensible heat flux and 10 W m−2 for latent heat flux). Actual runoff at the site is believed to be dominated by vertical drainage to groundwater, but several schemes produced significant amounts of runoff as overland flow or interflow. There is a range across schemes of 184 mm (40% of total pore volume) in the simulated annual mean root-zone soil moisture. Unfortunately, no measurements of soil moisture were available for model evaluation. A theoretical analysis suggested that differences in boundary conditions used in various schemes are not sufficient to explain the large variance in soil moisture. However, many of the extreme values of soil moisture could be explained in terms of the particulars of experimental setup or excessive evapotranspiration.

Full access
R. D. Koster
,
S. P. P. Mahanama
,
T. J. Yamada
,
Gianpaolo Balsamo
,
A. A. Berg
,
M. Boisserie
,
P. A. Dirmeyer
,
F. J. Doblas-Reyes
,
G. Drewitt
,
C. T. Gordon
,
Z. Guo
,
J.-H. Jeong
,
W.-S. Lee
,
Z. Li
,
L. Luo
,
S. Malyshev
,
W. J. Merryfield
,
S. I. Seneviratne
,
T. Stanelle
,
B. J. J. M. van den Hurk
,
F. Vitart
, and
E. F. Wood

Abstract

The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.

Full access
H. Lievens
,
A. Al Bitar
,
N. E. C. Verhoest
,
F. Cabot
,
G. J. M. De Lannoy
,
M. Drusch
,
G. Dumedah
,
H.-J. Hendricks Franssen
,
Y. Kerr
,
S. K. Tomer
,
B. Martens
,
O. Merlin
,
M. Pan
,
M. J. van den Berg
,
H. Vereecken
,
J. P. Walker
,
E. F. Wood
, and
V. R. N. Pauwels

Abstract

The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

Full access