An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000-1849.

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000-1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941-2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.

Consistent multi-decadal variability in global temperature reconstructions and simulations over the Common Era.

Multi-decadal surface temperature changes may be forced by natural as well as anthropogenic factors, or arise unforced from the climate system. Distinguishing these factors is essential for estimating sensitivity to multiple climatic forcings and the amplitude of the unforced variability. Here we present 2,000-year-long global mean temperature reconstructions using seven different statistical methods that draw from a global collection of temperature-sensitive paleoclimate records. Our reconstructions display synchronous multi-decadal temperature fluctuations, which are coherent with one another and with fully forced CMIP5 millennial model simulations across the Common Era. The most significant attribution of pre-industrial (1300-1800 CE) variability at multi-decadal timescales is to volcanic aerosol forcing. Reconstructions and simulations qualitatively agree on the amplitude of the unforced global mean multi-decadal temperature variability, thereby increasing confidence in future projections of climate change on these timescales. The largest warming trends at timescales of 20 years and longer occur during the second half of the 20th century, highlighting the unusual character of the warming in recent decades.

No evidence for globally coherent warm and cold periods over the preindustrial Common Era.

Earth's climate history is often understood by breaking it down into constituent climatic epochs1. Over the Common Era (the past 2,000 years) these epochs, such as the Little Ice Age2-4, have been characterized as having occurred at the same time across extensive spatial scales5. Although the rapid global warming seen in observations over the past 150 years does show nearly global coherence6, the spatiotemporal coherence of climate epochs earlier in the Common Era has yet to be robustly tested. Here we use global palaeoclimate reconstructions for the past 2,000 years, and find no evidence for preindustrial globally coherent cold and warm epochs. In particular, we find that the coldest epoch of the last millennium-the putative Little Ice Age-is most likely to have experienced the coldest temperatures during the fifteenth century in the central and eastern Pacific Ocean, during the seventeenth century in northwestern Europe and southeastern North America, and during the mid-nineteenth century over most of the remaining regions. Furthermore, the spatial coherence that does exist over the preindustrial Common Era is consistent with the spatial coherence of stochastic climatic variability. This lack of spatiotemporal coherence indicates that preindustrial forcing was not sufficient to produce globally synchronous extreme temperatures at multidecadal and centennial timescales. By contrast, we find that the warmest period of the past two millennia occurred during the twentieth century for more than 98 per cent of the globe. This provides strong evidence that anthropogenic global warming is not only unparalleled in terms of absolute temperatures5, but also unprecedented in spatial consistency within the context of the past 2,000 years.

Possible causes of data model discrepancy in the temperature history of the last Millennium.

Model simulations and proxy-based reconstructions are the main tools for quantifying pre-instrumental climate variations. For some metrics such as Northern Hemisphere mean temperatures, there is remarkable agreement between models and reconstructions. For other diagnostics, such as the regional response to volcanic eruptions, or hemispheric temperature differences, substantial disagreements between data and models have been reported. Here, we assess the potential sources of these discrepancies by comparing 1000-year hemispheric temperature reconstructions based on real-world paleoclimate proxies with climate-model-based pseudoproxies. These pseudoproxy experiments (PPE) indicate that noise inherent in proxy records and the unequal spatial distribution of proxy data are the key factors in explaining the data-model differences. For example, lower inter-hemispheric correlations in reconstructions can be fully accounted for by these factors in the PPE. Noise and data sampling also partly explain the reduced amplitude of the response to external forcing in reconstructions compared to models. For other metrics, such as inter-hemispheric differences, some, although reduced, discrepancy remains. Our results suggest that improving proxy data quality and spatial coverage is the key factor to increase the quality of future climate reconstructions, while the total number of proxy records and reconstruction methodology play a smaller role.

Tambora 1815 as a test case for high impact volcanic eruptions: Earth system effects.

The eruption of Tambora (Indonesia) in April 1815 had substantial effects on global climate and led to the 'Year Without a Summer' of 1816 in Europe and North America. Although a tragic event-tens of thousands of people lost their lives-the eruption also was an 'experiment of nature' from which science has learned until today. The aim of this study is to summarize our current understanding of the Tambora eruption and its effects on climate as expressed in early instrumental observations, climate proxies and geological evidence, climate reconstructions, and model simulations. Progress has been made with respect to our understanding of the eruption process and estimated amount of SO2 injected into the atmosphere, although large uncertainties still exist with respect to altitude and hemispheric distribution of Tambora aerosols. With respect to climate effects, the global and Northern Hemispheric cooling are well constrained by proxies whereas there is no strong signal in Southern Hemisphere proxies. Newly recovered early instrumental information for Western Europe and parts of North America, regions with particularly strong climate effects, allow Tambora's effect on the weather systems to be addressed. Climate models respond to prescribed Tambora-like forcing with a strengthening of the wintertime stratospheric polar vortex, global cooling and a slowdown of the water cycle, weakening of the summer monsoon circulations, a strengthening of the Atlantic Meridional Overturning Circulation, and a decrease of atmospheric CO2. Combining observations, climate proxies, and model simulations for the case of Tambora, a better understanding of climate processes has emerged. WIREs Clim Change 2016, 7:569-589. doi: 10.1002/wcc.407 This article is categorized under: 1Paleoclimates and Current Trends > Paleoclimate.

The influence of sampling design on tree-ring-based quantification of forest growth.

Tree-rings offer one of the few possibilities to empirically quantify and reconstruct forest growth dynamics over years to millennia. Contemporaneously with the growing scientific community employing tree-ring parameters, recent research has suggested that commonly applied sampling designs (i.e. how and which trees are selected for dendrochronological sampling) may introduce considerable biases in quantifications of forest responses to environmental change. To date, a systematic assessment of the consequences of sampling design on dendroecological and-climatological conclusions has not yet been performed. Here, we investigate potential biases by sampling a large population of trees and replicating diverse sampling designs. This is achieved by retroactively subsetting the population and specifically testing for biases emerging for climate reconstruction, growth response to climate variability, long-term growth trends, and quantification of forest productivity. We find that commonly applied sampling designs can impart systematic biases of varying magnitude to any type of tree-ring-based investigations, independent of the total number of samples considered. Quantifications of forest growth and productivity are particularly susceptible to biases, whereas growth responses to short-term climate variability are less affected by the choice of sampling design. The world's most frequently applied sampling design, focusing on dominant trees only, can bias absolute growth rates by up to 459% and trends in excess of 200%. Our findings challenge paradigms, where a subset of samples is typically considered to be representative for the entire population. The only two sampling strategies meeting the requirements for all types of investigations are the (i) sampling of all individuals within a fixed area; and (ii) fully randomized selection of trees. This result advertises the consistent implementation of a widely applicable sampling design to simultaneously reduce uncertainties in tree-ring-based quantifications of forest growth and increase the comparability of datasets beyond individual studies, investigators, laboratories, and geographical boundaries.