Matthews and Solomon: Irreversible Does Not Mean Unavoidable

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

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

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

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”, Φ_ψ=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

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.

Polade: Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

Figure 2. Pattern correlation of the leading SVD mode of modeled and observed SST versus pattern correlation of the leading SVD mode of modeled and observed precipitation (panel a). Blue and red symbols represent CMIP3 and CMIP5 models, respectively. Grey contour lines indicate model skill (higher values mean greater skill; see text for details). The skill difference between CMIP5 and CMIP3 models (panel b; see text for details).
Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 modelsbr>

Abstract
Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate-change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of fourteen models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea-surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary factor in the improvement from CMIP3 to CMIP5.

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 modelsbr>

Suraj D. Polade1,*, Alexander Gershunov1, Daniel R. Cayan1,2, Michael D. Dettinger2, David W. Pierce1
DOI: 10.1002/grl.50491

©2013. American Geophysical Union. All Rights Reserved.

Annan and Hargreaves: A new global reconstruction of temperature changes at the Last Glacial Maximum

Fig. 1. Reconstruction of Last Glacial Maximum surface air temperature anomaly (C) based on multi-model regression. Proxy data are represented as coloured dots.
A new global reconstruction of temperature changes at the Last Glacial Maximum

Abstract. Some recent compilations of proxy data both on land and ocean (MARGO Project Members, 2009; Bartlein et al., 2011; Shakun et al., 2012), have provided a new opportunity for an improved assessment of the overall climatic state of the Last Glacial Maximum. In this paper, we combine these proxy data with the ensemble of structurally diverse state of the art climate models which participated in the PMIP2 project (Braconnot et al., 2007) to generate a spatially complete reconstruction of surface air (and sea surface) temperatures. We test a variety of approaches, and show that multiple linear regression performs well for this application. Our reconstruction is significantly different to and more accurate than previous approaches and we obtain an estimated global mean cooling of 4.0±0.8 C (95% CI).

A new global reconstruction of temperature changes at the Last Glacial Maximum
J. D. Annan and J. C. Hargreaves
Clim. Past, 9, 367-376, 2013
http://www.clim-past.net/9/367/2013/
doi:10.5194/cp-9-367-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.

Stocker: Multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios

Fig 4: a,b, Simulated cN2O (a) and cCH4 (b) in the fully coupled (ranges) and the control (dashed lines) simulations for RCP2.6 (blue) and RCP8.5 (red). Results are from online simulations. Higher concentrations in the fully coupled simulations are due to amplified emissions in response to changes in both climate and cCO2. c, Additional radiative forcing due the higher cN2O and cCH4 in the fully coupled simulation compared with the control simulation. Changes in cCH4concentrations affect stratospheric cH2O and cO3. Resulting radiative forcings are included in RF(CH4). d, Global mean temperature change in the control simulation (dashed line), the fully coupled simulation (upper range), and the fully coupled simulation without changes in eN2O and eCH4 affecting climate (lower, pale-coloured range) for RCP2.6 (blue) and RCP8.5 (red). Grey lines represent ?T as simulated by the ensemble of CMIP5 models applied.
Multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios

Atmospheric concentrations of the three important greenhouse gases (GHGs) CO2, CH4 and N2O are mediated by processes in the terrestrial biosphere that are sensitive to climate and CO2. This leads to feedbacks between climate and land and has contributed to the sharp rise in atmospheric GHG concentrations since pre-industrial times. Here, we apply a process-based model to reproduce the historical atmospheric N2O and CH4 budgets within their uncertainties and apply future scenarios for climate, land-use change and reactive nitrogen (Nr) inputs to investigate future GHG emissions and their feedbacks with climate in a consistent and comprehensive framework1. Results suggest that in a business-as-usual scenario, terrestrial N2O and CH4 emissions increase by 80 and 45%, respectively, and the land becomes a net source of C by AD 2100. N2O and CH4 feedbacks imply an additional warming of 0.4–0.5?°C by AD 2300; on top of 0.8–1.0?°C caused by terrestrial carbon cycle and Albedo feedbacks. The land biosphere represents an increasingly positive feedback to anthropogenic climate change and amplifies equilibrium climate sensitivity by 22–27%. Strong mitigation limits the increase of terrestrial GHG emissions and prevents the land biosphere from acting as an increasingly strong amplifier to anthropogenic climate change.

Multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios
Benjamin D. Stocker, Raphael Roth, Fortunat Joos, Renato Spahni, Marco Steinacher, Soenke Zaehle, Lex Bouwman, Xu-Ri & Iain Colin Prentice
Nature Climate Change (2013) doi:10.1038/nclimate1864
Received 30 July 2012 Accepted 01 March 2013 Published online 14 April 2013

Lunt: A multi-model assessment of last interglacial temperatures

Fig. 6. Simulated surface air temperature change, LIG minus pre-industrial, for the model ensemble. (a) Annual mean, (b) DJF, (c) JJA, and (d) warm-month mean (WMM). Stippled regions show regions where less than 70% of the model simulations agree on the sign of the temperature change. Also shown are the terrestrial data points of Turney and Jones (2010).
A multi-model assessment of last interglacial temperatures

Abstract. The last interglaciation (~130 to 116 ka) is a time period with a strong astronomically induced seasonal forcing of insolation compared to the present. Proxy records indicate a significantly different climate to that of the modern, in particular Arctic summer warming and higher eustatic sea level. Because the forcings are relatively well constrained, it provides an opportunity to test numerical models which are used for future climate prediction. In this paper we compile a set of climate model simulations of the early last interglaciation (130 to 125 ka), encompassing a range of model complexities. We compare the simulations to each other and to a recently published compilation of last interglacial temperature estimates. We show that the annual mean response of the models is rather small, with no clear signal in many regions. However, the seasonal response is more robust, and there is significant agreement amongst models as to the regions of warming vs cooling. However, the quantitative agreement of the model simulations with data is poor, with the models in general underestimating the magnitude of response seen in the proxies. Taking possible seasonal biases in the proxies into account improves the agreement, but only marginally. However, a lack of uncertainty estimates in the data does not allow us to draw firm conclusions. Instead, this paper points to several ways in which both modelling and data could be improved, to allow a more robust model-data comparison.

A multi-model assessment of last interglacial temperatures
D. J. Lunt, A. Abe-Ouchi, P. Bakker, A. Berger, P. Braconnot, S. Charbit, N. Fischer, N. Herold, J. H. Jungclaus, V. C. Khon, U. Krebs-Kanzow, P. M. Langebroek, G. Lohmann, K. H. Nisancioglu, B. L. Otto-Bliesner, W. Park, M. Pfeiffer, S. J. Phipps, M. Prange, R. Rachmayani, H. Renssen, N. Rosenbloom, B. Schneider, E. J. Stone, K. Takahashi, W. Wei, Q. Yin, and Z. S. Zhang
Clim. Past, 9, 699-717, 2013
http://www.clim-past.net/9/699/2013/
doi:10.5194/cp-9-699-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.

Hargreaves: Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene

Fig. 6. Taylor diagrams for the LGM mean temperature anomaly and MH hottest month anomaly. Distance from origin indicates standard deviation of field, distance from reference point indicates centred RMS difference between model and data, and pattern correlation is given by the azimuthal coordinate. The left plot shows conventional analysis, with the location of the “perfect model” indicated for comparison. The right plot shows the analysis where model statistics are corrected to account for observational errors. All results are normalised by the standard deviation of the data fields.
Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene

Abstract. Paleoclimate simulations provide us with an opportunity to critically confront and evaluate the performance of climate models in simulating the response of the climate system to changes in radiative forcing and other boundary conditions. Hargreaves et al. (2011) analysed the reliability of the Paleoclimate Modelling Intercomparison Project, PMIP2 model ensemble with respect to the MARGO sea surface temperature data synthesis (MARGO Project Members, 2009) for the Last Glacial Maximum (LGM, 21 ka BP). Here we extend that work to include a new comprehensive collection of land surface data (Bartlein et al., 2011), and introduce a novel analysis of the predictive skill of the models. We include output from the PMIP3 experiments, from the two models for which suitable data are currently available. We also perform the same analyses for the PMIP2 mid-Holocene (6 ka BP) ensembles and available proxy data sets.
Our results are predominantly positive for the LGM, suggesting that as well as the global mean change, the models can reproduce the observed pattern of change on the broadest scales, such as the overall land–sea contrast and polar amplification, although the more detailed sub-continental scale patterns of change remains elusive. In contrast, our results for the mid-Holocene are substantially negative, with the models failing to reproduce the observed changes with any degree of skill. One cause of this problem could be that the globally and annually-averaged forcing anomaly is very weak at the mid-Holocene, and so the results are dominated by the more localised regional patterns in the parts of globe for which data are available. The root cause of the model-data mismatch at these scales is unclear. If the proxy calibration is itself reliable, then representativity error in the data-model comparison, and missing climate feedbacks in the models are other possible sources of error.

Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene
J. C. Hargreaves, J. D. Annan, R. Ohgaito, A. Paul, and A. Abe-Ouchi
Clim. Past, 9, 811-823, 2013
http://www.clim-past.net/9/811/2013/
doi:10.5194/cp-9-811-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.