Tropical cyclones that rapidly intensify are typically associated with the highest forecast errors and cause a disproportionate amount of human and financial losses. Therefore, it is crucial to ...understand if, and why, there are observed upward trends in tropical cyclone intensification rates. Here, we utilize two observational datasets to calculate 24-hour wind speed changes over the period 1982-2009. We compare the observed trends to natural variability in bias-corrected, high-resolution, global coupled model experiments that accurately simulate the climatological distribution of tropical cyclone intensification. Both observed datasets show significant increases in tropical cyclone intensification rates in the Atlantic basin that are highly unusual compared to model-based estimates of internal climate variations. Our results suggest a detectable increase of Atlantic intensification rates with a positive contribution from anthropogenic forcing and reveal a need for more reliable data before detecting a robust trend at the global scale.
Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall ...under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting ...broad impacts on weather and precipitation patterns around the globe. Recently, Booth et al. showed that the Hadley Centre Global Environmental Model, version 2, Earth system configuration (HadGEM2-ES) closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the twentieth century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al. concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper-ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anticorrelation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and ...observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Although fullerenes were discovered nearly three decades ago, the mechanism of their formation remains a mystery. Many versions of the classic 'bottom-up' formation mechanism have been advanced, ...starting with C2 units that build up to form chains and rings of carbon atoms and ultimately form those well-known isolated fullerenes (for example, I(h)-C60). In recent years, evidence from laboratory and interstellar observations has emerged to suggest a 'top-down' mechanism, whereby small isolated fullerenes are formed via shrinkage of giant fullerenes generated from graphene sheets. Here, we present molecular structural evidence for this top-down mechanism based on metal carbide metallofullerenes M2C2@C1(51383)-C84 (M = Y, Gd). We propose that the unique asymmetric C1(51383)-C84 cage with destabilizing fused pentagons is a preserved 'missing link' in the top-down mechanism, and in well-established rearrangement steps can form many well-known, high-symmetry fullerene structures that account for the majority of solvent-extractable metallofullerenes.
Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim ...to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.
Responses of tropical cyclones (TCs) to CO
2
doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ ...25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO
2
TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO
2
-induced warming patterns and CO
2
doubling. Isolated CO
2
doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO
2
concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from water-sheds, rivers, and the coastal ocean. Climate change has potential to modify the ...physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO₂ emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2-5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.
A coupled climate model with poleward-intensified westerly winds simulates significantly higher storage of heat and anthropogenic carbon dioxide by the Southern Ocean in the future when compared with ...the storage in a model with initially weaker, equatorward-biased westerlies. This difference results from the larger outcrop area of the dense waters around Antarctica and more vigorous divergence, which remains robust even as rising atmospheric greenhouse gas levels induce warming that reduces the density of surface waters in the Southern Ocean. These results imply that the impact of warming on the stratification of the global ocean may be reduced by the poleward intensification of the westerlies, allowing the ocean to remove additional heat and anthropogenic carbon dioxide from the atmosphere.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to ...address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions.
Statistical downscaling techniques are frequently used to translate changes from global climate models to local scales. However, there is no standard practice with respect to downscaling technique or creating the gridded observations used for training. This study demonstrates that multiple precipitation variables are sensitive to the training observations and the downscaling technique. Depending upon the variable of interest and the application, end users of downscaled projections should consider these factors in their own efforts.
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