Archive

Author Archive

McKinnon: The spatial structure of the annual cycle in surface temperature: amplitude, phase, and Lagrangian history

2013 May 9 Comments off


Fig. 7. (a) Monthly temperature anomalies in the latitude band 45-50!N from the advection model driven by HYSPLIT trajectories versus observations. (b) The gain and lag of the modeled annual cycle in polar coordinates showing land (X’s) and ocean (O’s) boxes. Neighboring gridboxes are connected via a thin gray line. (c) The gain of the modeled annual cycle across longitude at 45-50!N using a zonal wind (gray) and with the inclusion of the HYSPLIT trajectory information (black), as compared to the observations (dashed). Land regions are indicated by shading. (d) Similar to (c) but for lag.

The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of our climate system. Here, we identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag (1) are broadly consistent with a land and ocean end-member mixing model, but (2) exhibit overlap between land and ocean, and, despite this overlap, (3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, we use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space-time variance in the extratropical annual cycle, as well as features (1) and (2). All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space-time variance of the annual cycle in an illustrative mid-latitude zonal band when incorporating the results of the trajectory model. The basic features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

The spatial structure of the annual cycle in surface temperature: amplitude, phase, and Lagrangian history
Karen A. McKinnon, Alexander R. Stine, and Peter Huybers
Journal of Climate 2013 ; e-View
doi: http://dx.doi.org/10.1175/JCLI-D-13-00021.1

Alternate Source:

http://www.people.fas.harvard.edu/~phuybers/Doc/McKinnon_JofC2013.pdf

Lehner: Amplified inception of European Little Ice Age by sea ice-ocean-atmosphere feedbacks

2013 May 8 Comments off


Fig. 9. Schematic overview of the feedback loops associated with the Medieval Climate Anomaly-Little Ice Age transition: decreasing external forcing leads to increased sea ice in the Arctic, especially in the Barents Sea. Loop 1: this causes an increased Arctic sea ice export and subsequently an increased import of sea ice into the Labrador Sea. As this sea ice melts, it weakens the Atlantic Meridional Overturning Circulation (AMOC), which in turn reduces the Barents Sea inflow of warm waters, causing further sea ice growth. Loop 2: increased sea ice causes the Barents Sea to become fresher and less dense. Also, wind changes due to elevated sea level pressure (SLP) increase the sea surface height (SSH) in the Barents Sea. As a result of these two processes, the SSH gradient across the Barents Sea opening increases, further reducing the Barents Sea inflow and thereby supporting sea ice growth. Finally, the increased sea ice cover has a direct thermal effect, decreasing surface air temperatures over Northern Europe and an indirect effect by inducing elevated sea level pressure (SLP) that advects cold Arctic air towards Europe.
Amplified inception of European Little Ice Age by sea ice-ocean-atmosphere feedbacks

The inception of the Little Ice Age (~1400-1700 AD) is believed to have been driven by an interplay of external forcing and climate system-internal variability. While the hemispheric signal seems to have been dominated by solar irradiance and volcanic eruptions, the understanding of mechanisms shaping the climate on continental scale is less robust. In an ensemble of transient model simulations and a new type of sensitivity experiments with artificial sea ice growth we identify a sea ice-ocean-atmosphere feedback mechanism that amplifies the Little Ice Age cooling in the North Atlantic-European region and produces the temperature pattern suggested by paleoclimatic reconstructions. Initiated by increasing negative forcing, the Arctic sea ice substantially expands at the beginning of the Little Ice Age. The excess of sea ice is exported to the subpolar North Atlantic, where it melts, thereby weakening convection of the ocean. Consequently, northward ocean heat transport is reduced, reinforcing the expansion of the sea ice and the cooling of the Northern Hemisphere. In the Nordic Seas, sea surface height anomalies cause the oceanic recirculation to strengthen at the expense of the warm Barents Sea inflow, thereby further reinforcing sea ice growth. The absent ocean-atmosphere heat flux in the Barents Sea results in an amplified cooling over Northern Europe. The positive nature of this feedback mechanism enables sea ice to remain in an expanded state for decades up to a century, favoring sustained cold periods over Europe such as the Little Ice Age. Support for the feedback mechanism comes from recent proxy reconstructions around the Nordic Seas.

Amplified inception of European Little Ice Age by sea ice-ocean-atmosphere feedbacks
Flavio Lehner, Andreas Born, Christoph C. Raible, and Thomas F. Stocker
Journal of Climate 2013 ; e-View
doi: http://dx.doi.org/10.1175/JCLI-D-12-00690.1

Alternate source:

http://www.climate.unibe.ch/~born/publications/mcalia_seaice.pdf

Wang and Zeng: Development of global hourly 0.5-degree land surface air temperature datasets

2013 May 7 8 comments



Figures extracted from a presentation at the AMS 25th Conference on Climate Variability and Change

https://ams.confex.com/ams/93Annual/webprogram/Paper217099.html

Land surface air temperature (SAT) is one of the most important variables in weather and climate studies, and its diurnal cycle and day-to-day variation are also needed for a variety of applications. Global long-term hourly SAT observational data, however, do not exist. While such hourly products could be obtained from global reanalyses, they are strongly affected by model parameterizations and hence are found to be unrealistic in representing the SAT diurnal cycle (even after the monthly mean bias correction).

Global hourly 0.5-degree SAT datasets are developed here based on four reanalysis products [MERRA (1979-2009), ERA-40 (1958-2001), ERA-Interim (1979-2009), and NCEP/NCAR (1948-2009)] and the CRU TS3.10 data for 1948-2009. Our three-step adjustments include the spatial downscaling to 0.5-degree grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP/NCAR) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the mean bias correction in both monthly mean maximum and minimum SAT using the CRU data.

The final products have exactly the same monthly maximum and minimum SAT as the CRU data, and perform well in comparison with in situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in our final products can be quantified by their differences in the true monthly mean (using 24 hourly values) and the monthly averaged diurnal cycle.

Development of global hourly 0.5-degree land surface air temperature datasets
Wang and Zeng
Journal of Climate 2013 ; e-View
doi: http://dx.doi.org/10.1175/JCLI-D-12-00682.1

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00682.1

Kapsch: Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent

2013 May 6 Comments off


Figure S5: Radiative and turbulent flux anomalies at the surface for LIYs from
NCEP-DOE R2. The black line shows the sea-ice concentration (ERA-Interim reanalysis).
a, displayed is the net longwave radiation plus the turbulent fluxes (latent
and sensible; in red) and the net shortwave radiation (green). b, the radiative fluxes
are split into their components but only downwelling longwave (red) and shortwave
(green) radiation are shown together with the latent (dark blue) and sensible (light
blue) heat flux. All time series are based on daily anomalies of LIYs and averaged
over the area indicated by the red box in Supplementary Fig. 2. A 30-day runningmean
filter is applied to all time series.

Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent

The summer sea-ice extent in the Arctic has decreased in recent decades, a feature that has become one of the most distinct signals of the continuing climate change1, 2, 3, 4. However, the inter-annual variability is large—the ice extent by the end of the summer varies by several million square kilometres from year to year5. The underlying processes driving this year-to-year variability are not well understood. Here we demonstrate that the greenhouse effect associated with clouds and water vapour in spring is crucial for the development of the sea ice during the subsequent months. In years where the end-of-summer sea-ice extent is well below normal, a significantly enhanced transport of humid air is evident during spring into the region where the ice retreat is encountered. This enhanced transport of humid air leads to an anomalous convergence of humidity, and to an increase of the cloudiness. The increase of the cloudiness and humidity results in an enhancement of the greenhouse effect. As a result, downward long-wave radiation at the surface is larger than usual in spring, which enhances the ice melt. In addition, the increase of clouds causes an increase of the reflection of incoming solar radiation. This leads to the counter-intuitive effect: for years with little sea ice in September, the downwelling short-wave radiation at the surface is smaller than usual. That is, the downwelling short-wave radiation is not responsible for the initiation of the ice anomaly but acts as an amplifying feedback once the melt is started.

Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent
Marie-Luise Kapsch, Rune Grand Graversen & Michael Tjernström
Nature Climate Change
doi:10.1038/nclimate1884

http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate1884.html

Zaman: A Bayesian Approach for Predicting the Popularity of Tweets

2013 May 3 Comments off


FIG 7. Graphical model of the Bayesian log-normal-binomial model for the evolution of retweet graphs. Hyper-priors are omitted for simplicity. The plates denote replication over tweets x and users vxj.

We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or “graph” structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e. less than one tenth) of the retweet paths are observed. This translates to good predictions within a few minutes of a tweet being posted and has potential implications for understanding the spread of broader ideas, memes, or trends in social networks and also revenue models for both individuals who “sell tweets” and for those looking to monetize their reach.

A Bayesian Approach for Predicting the Popularity of Tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow
arXiv:1304.6777 [cs.SI]

Matthews and Solomon: Irreversible Does Not Mean Unavoidable

2013 May 2 Comments off

Understanding how decreases in CO2 emissions would affect global temperatures has been hampered in recent years by confusion regarding issues of committed warming and irreversibility. The notion that there will be additional future warming or “warming in the pipeline” if the atmospheric concentrations of carbon dioxide were to remain fixed at current levels (1) has been misinterpreted to mean that the rate of increase in Earth’s global temperature is inevitable, regardless of how much or how quickly emissions decrease (2–4). Further misunderstanding may stem from recent studies showing that the warming that has already occurred as a result of past anthropogenic carbon dioxide increases is irreversible on a time scale of at least 1000 years (5, 6). But irreversibility of past changes does not mean that further warming is unavoidable

Given the irreversibility of CO2-induced warming, every increment of avoided temperature increase represents less warming that would otherwise persist for many centuries. Although emissions reductions cannot return global temperatures to preindustrial levels, they do have the power to avert additional warming on the same time scale as the emissions reductions themselves. Climate warming tomorrow, this year, this decade, or this century is not predetermined by past CO2 emissions; it is yet to be determined by future emissions. The climate benefits of emissions reductions would thus occur on the same time scale as the political decisions that lead to the reductions.

Irreversible Does Not Mean Unavoidable
H. Damon Matthews, Susan Solomon
Published Online March 28 2013
Science 26 April 2013:
Vol. 340 no. 6131 pp. 438-439
DOI: 10.1126/science.1236372

Ólafsdóttir: Evolution of NAO and AMO strength and cyclicity derived from a 3-ka varve-thickness record from Iceland

2013 May 1 Comments off


Fig. 4. MTM spectrum for core HVT03-2 for the past ∼3000 year (982 BC–2002 AD at annual resolution). Prior to the analysis the time series was detrended by subtracting the first two SSA components, that define a ∼1000-year cycle and a ∼500-year cycle, from the original series. The power spectrum is shown along with 90, 95 and 99% confidence levels. Spectral peaks of 290, 130, 55, 35, 14, 13, 5, 4.4 and 2.8 years are indicated in the MTM spectrum.
Evolution of NAO and AMO strength and cyclicity derived from a 3-ka varve-thickness record from Iceland

A 3000-year varve-thickness record from Hvítárvatn, a glacier-dominated lake in central Iceland, preserves inter-annual variations in the delivery of glacially eroded sediment to the lake. The first-order low-frequency trend in varve thickness reflects increased glacial erosion through the Late Holocene, reaching a peak during the Little Ice Age (LIA). Superimposed on this trend are large inter-annual to decadal fluctuations in varve thickness that we suggest reflect variability in climate parameters that determine the efficiency of the fluvial transport system to deliver glacially eroded sediment to the lake each year. We use spectral analysis to test whether regular high-frequency cyclicity in varve thickness exists in the 3-ka record after removing the low-frequency variability. Spectral analyses from three sediment cores recovered from the lake show essentially the same periods of 2.8–3.4, 13, 35–40 and 85–93, for the overlapping ∼900-year period. Additionally, cycles of 55, 130 and 290 years are found in the spectrum for the 3000-year record that do not show up in the spectra for the shorter cores. Some of these cycles show similar variability to those of the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO). This relationship is supported by a significant correlation between varve thickness and both the NAO (precipitation) and AMO (summer temperature) indices over the 180-year instrumental period. NAO cyclicities (2–15 years) are weakly expressed in the first half of the record, increase between 600 and 1000 AD, decrease in strength during medieval time, and are most strongly expressed between 1300 AD and the early 20th century. AMO cyclicities (50 to 130 years) are also relatively weak in the first half of the record, becoming quite strong between 600 and 1000 AD and again between 1100 and 1500 AD, but are essentially absent through the peak of the LIA, between 1500 and 1900 AD, a time when strong cyclicities of about 35 years appear.

Evolution of NAO and AMO strength and cyclicity derived from a 3-ka varve-thickness record from Iceland
Kristín B. Ólafsdóttir, Áslaug Geirsdóttir, Gifford H. Miller, Darren J. Larsen
Quaternary Science Reviews
Volume 69, 1 June 2013, Pages 142–154

http://dx.doi.org/10.1016/j.quascirev.2013.03.009

Geirsdóttir: Abrupt Holocene climate transitions in the northern North Atlantic region recorded by synchronized lacustrine records in Iceland

2013 April 30 Comments off


Fig. 10. Composite proxy records from HAK and HVT compared to the Renland ice core record (normalized δ18O record and its variance) from Greenland (Vinther et al., 2008); Quartz% (normalized) from MD99-2269 as an indicator of sea ice in the northern North Atlantic (Moros et al., 2006) and 65°N summer insolation (Berger and Loutre, 1991). (modified)

Abrupt Holocene climate transitions in the northern North Atlantic region recorded by synchronized lacustrine records in Iceland

Two high-sediment-accumulation-rate Icelandic lakes, the glacial lake Hvítárvatn and the non-glacial lake Haukadalsvatn, contain numerous tephra layers of known age, which together with high-resolution paleomagnetic secular variations allow synchronization with a well-dated marine core from the shelf north of Iceland. A composite standardized climate record from the two lakes provides a single time series that efficiently integrates multi-proxy data that reflect the evolution of summer temperatures through the Holocene. The first-order trends in biogenic silica (BSi), δ13C, and C:N rise relatively abruptly following deglaciation, reaching maximum values shortly after 8 ka following a complex minimum between 8.7 and 8.0 ka. The Holocene Thermal Maximum (HTM) in the lakes is marked by all proxies, with a sharp transition out of the 8 ka cold event into peak summer warmth by 7.9 ka, and continuing warm with some fluctuations until 5.5 ka. Decreasing summer insolation after the HTM is reflected by incremental cooling, initially ∼5.5 ka, with subsequent cold perturbations recorded by all proxies 4.3 to 4.0 ka and 3.1 to 2.8 ka. The strongest disturbance occurred after 2 ka with initial summer cooling occurring between 1.4 and 1.0 ka, followed by a more severe drop in summer temperatures after 0.7 ka culminating between 0.5 and 0.2 ka. Following each late Holocene cold departure, BSi re-equilibrated at a lower value independent of the sediment accumulation rate. Some of the abrupt shifts may be related to Icelandic volcanism influencing catchment stability, but the lack of a full recovery to pre-existing values after the perturbation suggests increased periglacial activity, decreased vegetation cover, and glacier growth in the highlands of Iceland. The similarity in timing, direction and magnitude of our multi-proxy records from glacial and non-glacial lakes, and from the adjacent marine shelf, suggests that our composite record reflects large-scale shifts in ocean/atmosphere circulation throughout the northern North Atlantic.

Abrupt Holocene climate transitions in the northern North Atlantic region recorded by synchronized lacustrine records in Iceland
Áslaug Geirsdóttir, Gifford H. Miller, Darren J. Larsen, Sædís Ólafsdóttir

http://dx.doi.org/10.1016/j.quascirev.2013.03.010

Quaternary Science Reviews
Volume 70, 15 June 2013, Pages 48–62

Kang: Uncertainty in climate change projections of the Hadley circulation: the role of internal variability

2013 April 29 Comments off


Fig. 1. (a,c) CCSM3 40-member ensemble mean ψ climatology (black contours) and trends
(colors). Positive values (red shading and solid contours) indicate clockwise circulation; negative
values (blue shading and dashed contours) contour-clockwise circulation. Black contour
interval: 51010 kg/s. (b,d) Nmin, the minimum number of ensemble members needed to
detect a signi cant trends. Gray areas indicate locations where trends are not signi cant at
the 95% con dence level. In all panels the climatological latitudes max are marked with a
“x”, Φψ=0 with a “+”, and Pt with a horizontal line segment in each hemisphere.
Left panels show DJF, right panels JJA.

Uncertainty in climate change projections of the Hadley circulation: the role of internal variability

The uncertainty arising from internal climate variability in climate change projections of the Hadley circulation (HC) is presently unknown. In this paper it is quantified by analyzing a 40-member ensemble of integrations of the Community Climate System Model, Version 3 (CCSM3) under the SRES A1B scenario over the period 2000–2060. An additional set of 100 year-long, time-slice integrations with the atmospheric component of the same model (CAM3) is also analyzed.

Focusing on simple metrics of the HC – its strength, width and height – three key results emerge from our analysis of the CCSM3 ensemble. First, the projected weakening of the HC is almost entirely confined to the Northern Hemisphere, and is stronger in winter than summer. Second, the projected widening of the HC occurs only in the winter season, but in both hemispheres. Third, the projected rise of the tropical tropopause occurs in all hemispheres and in all seasons and is, by far, the most robust of the three metrics.

We show further that uncertainty in future trends of HC width is largely controlled by extratropical variability, while those of HC strength and height are associated primarily with tropical dynamics. Comparison of the CCSM3 and CAM3 integrations reveals that ocean-atmosphere coupling is the dominant source of uncertainty in future trends of HC strength and height, and of the tropical mean meridional circulation in general. Finally, we show that uncertainty in future trends of the hydrological cycle is largely captured by the uncertainty in future trends of the mean meridional circulation

Uncertainty in climate change projections of the Hadley circulation: the role of internal variability
Sarah M. Kang, Clara Deser, Lorenzo M. Polvani

Journal of Climate 2013 ; e-View
doi: http://dx.doi.org/10.1175/JCLI-D-12-00788.1

Folland: High predictive skill of global surface temperature a year ahead

2013 April 26 Comments off


Figure 1. Performance of real-time issued forecasts, 2000–2011. Performance of issued forecasts relative to the contemporary version of HadCRUT. The separate statistical and dynamical forecast components since 2008 are shown. HadCRUT4 values (blue dashed) illustrate the cold bias in the original observations from 2004. Uncertainties are not shown for clarity.
High predictive skill of global surface temperature a year ahead

We discuss 13 real-time forecasts of global annual-mean surface temperature issued by the United Kingdom Met Office for 1 year ahead for 2000–2012. These involve statistical, and since 2008, initialized dynamical forecasts using the Met Office DePreSys system. For the period when the statistical forecast system changed little, 2000–2010, issued forecasts had a high correlation of 0.74 with observations and a root mean square error of 0.07°C. However, the HadCRUT data sets against which issued forecasts were verified were biased slightly cold, especially from 2004, because of data gaps in the strongly warming Arctic. This observational cold bias was mainly responsible for a statistically significant warm bias in the 2000–2010 forecasts of 0.06°C. Climate forcing data sets used in the statistical method, and verification data, have recently been modified, increasing hindcast correlation skill to 0.80 with no significant bias. Dynamical hindcasts for 2000–2011 have a similar correlation skill of 0.78 and skillfully hindcast annual mean spatial global surface temperature patterns. Such skill indicates that we have a good understanding of the main factors influencing global mean surface temperature.

High predictive skill of global surface temperature a year ahead
Chris K. Folland, Andrew W. Colman, Doug M. Smith, Olivier Boucher, David E. Parker, Jean-Paul Vernier
Article first published online: 27 FEB 2013

DOI: 10.1002/grl.50169

©2013. American Geophysical Union. All Rights Reserved.

Follow

Get every new post delivered to your Inbox.

Join 27 other followers