2019

1. [Coronesea et al. 2019] ABSTRACT: Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to cap- ture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tail- fattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically sig- nificant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme dam- ages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative.
   Economic cost natural disasters

2. [Hoegh-Guldberg et al. 2019] ABSTRACT: Increased concentrations of atmospheric greenhouse gases have led to a global mean surface temperature 1.0C higher than during the pre-industrial period. We expand on the recent IPCC Special Report on global warming of 1.5C and review the additional risks associated with higher levels of warming, each having major implications for multiple geographies, climates, and ecosystems. Limiting warming to 1.5 C rather than 2.0C would be required to maintain substantial proportions of ecosystems and would have clear benefits for human health and economies. These conclusions are relevant for people everywhere, particularly in low- and middle-income countries, where the escalation of climate-related risks may prevent the achievement of the United Nations Sustainable Development Goals.
   1.5 limit

3. [Lucio et al. 2019] ABSTRACT: The variability of the surface wind eld over Northeastern North America was analysed through a statistical downscaling (SD) approach, using the relationships among the main large-scale and observed wind circulation modes. The large-scale variables were provided by 12 global reanalyses. The observed zonal and meridional wind components come from a data- base of 525 sites spanning over 1953–2010. A large percentage of the regional variability was explained in terms of three major large- and regional/local-scale coupled circulation patterns, accounting for 55.3% (59.3%) of the large (regional/local) scale variability. The method delivered robust results regardless of the SD model con guration, albeit with sensitivity to the number of retained circulation modes and the large-scale window size, but not to the reanalysis chosen for the large-scale variables. The methodological uncertainty was larger for sites/wind components with larger variability. A parameter con- guration chosen for yielding the best possible SD estimations showed high correlation values between these estimations and the observations for the majority of the sites (0.6–0.9, signi cant at p < 0.05), and a realistic wind variance (standard deviation ratios between 0.6 and 1.0), with similar results regardless of the reanalysis. The reanalysis direct wind outputs showed higher correlations than the SD estimates (0.7–0.97, also signi cant). The skill in reproducing observational variance di ered considerably from model to model (ratios between 0.5 and 3). The regional wind climatology was reconstructed back to 1850 with the help of century long reanalyses and two additional SLP gridded datasets allowing to estimate the variability at decadal and multidecadal timescales. Recent trends in the wind components are not unusual in the context of century-long reconstructed variability. Extreme values in both components tend to appear associated with high values in the rst two modes of variability.
   Statistical downscaling of wind

4. [Manabe 2019] ABSTRACT: Climate models have become the most powerful tool not only for predicting climate change but also for understanding it. Here, I discuss the role of greenhouse gases such as carbon dioxide and water vapour in global warming, using a hierarchy of climate models with increasing complexity.
   Greenhouse warming, modelling

5. [Neukom et al. 2019] ABSTRACT: 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.
   Last millennium

6. [Palmer and Stevens 2019] ABSTRACT: Given the slow unfolding of what may become catastrophic changes to Earth’s climate, many are under- standably distraught by failures of public policy to rise to the magnitude of the challenge. Few in the science community would think to question the scientific response to the unfolding changes. However, is the science community continuing to do its part to the best of its ability? In the domains where we can have the greatest influence, is the scientific community articulating a vision commensurate with the challenges posed by climate change? We think not.
   Challenge of climate modelling

7. [2k 2019] ABSTRACT: Multidecadal 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 palaeoclimate records. Our recon- structions display synchronous multidecadal temperature fluctuations that are coherent with one another and with fully forced millennial model simulations from the Coupled Model Intercomparison Project Phase 5 across the Common Era. A substantial portion of pre-industrial (1300–1800 ce) variability at multidecadal timescales is attributed to volcanic aerosol forcing. Reconstructions and simulations qualitatively agree on the amplitude of the unforced global mean multidecadal 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 twentieth century, highlighting the unusual character of the warming in recent decades.
   Multidecadal climate variability last millennium

8. [Ramaswamy et al. 2019b] ABSTRACT: We describe the historical evolution of the conceptualization, formulation, quantification, application, and utilization of ‘‘radiative forcing’’ (RF) of Earth’s climate
   RF, ERF

9. [Sinha et al. 2019] Abstract: Northern Iraq was the political and economic center of the Neo-Assyrian Empire (c. 912 to 609 BCE) the largest and most powerful empire of its time. After more than two centuries of regional dominance, the Neo-Assyrian state plummeted from its zenith (c. 670 BCE) to complete political collapse (c. 615 to 609 BCE). Earlier explanations for the Assyrian collapse focused on the roles of internal politico-economic conflicts, territorial overextension, and military defeat. Here, we present a high-resolution and precisely dated speleothem record of climate change from the Kuna Ba cave in northern Iraq, which suggests that the empires rise occurred during a two-centuries-long interval of anomalously wet climate in the context of the past 4000 years, while megadroughts during the early-mid seventh century BCE, as severe as recent droughts in the region but lasting for decades, triggered a decline in Assyrias agrarian productivity and thus contributed to its eventual political and economic collapse.
   Assyrian demis

10. [Yöstaló 2019] Abstract: This article concerns the recent shifts and tensions in the role of scienti c knowledge in policymaking. Policy practitioners are striving to solve a compelling problem: how to deal with complex problems under conditions of uncertainty. This article focuses on two policy reforms in Finland that have been designed to address these issues: strategic governance reform and the take up of a culture of experimentation. It suggests that the knowledge-policy relations within these reforms are characterised by profound tensions between ‘good governance’ and ‘good knowledge’. Whereas good governance demands that knowledge is controlled, good knowledge entails uncertain and uncontrollable elements.
    Policy, science and policy