(2017), Smaller desert dust cooling effect estimated from analysis of desert dust size and abundance, Nature Geoscience,10,[274][275][276][277][278] Desert dust aerosols affect Earth's global energy balance through direct interactions with radiation, and through indirect interactions with clouds and ecosystems. But the magnitudes of these effects are so uncertain that it remains unclear whether atmospheric dust has a net warming or cooling effect on global climate. Consequently, it is still uncertain whether large changes in atmospheric dust loading over the past century have slowed or accelerated anthropogenic climate change, or what the effects of potential future changes in dust loading will be. Here we present an analysis of the size and abundance of dust aerosols to constrain the direct radiative effect of dust. Using observational data on dust abundance, in situ measurements of dust optical properties and size distribution, and climate and atmospheric chemical transport model simulations of dust lifetime, we find that the dust found in the atmosphere is substantially coarser than represented in current global climate models. Since coarse dust warms climate, the global dust direct radiative effect is likely to be less cooling than the ~-0.4 W/m 2 estimated by models in a current global aerosol model ensemble. Instead, we constrain the dust direct radiative effect to a range between -0.48 and +0.20 W/m 2 , which includes the possibility that dust causes a net warming of the planet.The direct radiative effect (DRE) of desert dust aerosols on global climate depends sensitively on both the size distribution and atmospheric abundance of dust 1-3 . However, current global model estimates of the atmospheric loading of dust with geometric diameter D ≤ 10 µm (PM10) vary widely from ~6 to 30 Tg [4][5][6][7] . Similarly, the size distribution of atmospheric dust varies substantially across models, with the fraction of dust in the clay size range (D ≤ 2 µm) varying by over a factor of three 8 . This uncertainty in dust size and abundance is partially driven by a critical limitation of global models: the need to prescribe poorly known attributes of dust particles. In particular, the assumed dust optical properties and size distribution at emission greatly affect the resultant size-resolved dust loading 1,6 . Each model parameterizes these properties differently, and in a manner not always consistent with experimental results [8][9][10] . This divergence in assumed dust properties contributes to a wide range of estimates of the sizeresolved global dust loading 6,8 . Because fine dust cools global climate whereas coarse dust (D ≥ 5 μm) likely warms it 3 , this uncertainty in size-resolved dust loading contributes to a wide spread in model estimates of the dust DRE 1,3,9,[11][12][13][14] . Since the use of global models alone is thus unlikely to substantially narrow the uncertainty on dust climate effects 15 , we develop an alternative approach to determine the size-resolved global dust loading, which we subsequently use ...
Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. precipitation set a record (Fig. 3a). Sustained high precipitation amounts 60 during the whole winter led to this record, rather than a few very wet days, Human influence on climate in the 2014 Southern 61and none of the 5-day precipitation averages over the three winter months 62 was a record (Fig. 3b). Similarly, while Thames' daily peak river flows were 63 not exceptional, the 30-day peak flow was the second highest since 64 measurements began in 1883 ( Supplementary Fig. 10 to provide a conservative estimate of uncertainty. 106We consider January precipitation and SLP, with Southern England 107Precipitation (SEP) averaged over land grid points in 50º-52ºN, 6.5ºW-2ºE. 189In the large RCM ensemble, the best estimate for the overall change in risk of is an increase of 43%, with a range from no change to 164% increase 192 associated with uncertainty in the pattern of anthropogenic warming (Fig. 5d). rainfall that we simulate is less on timescales that dominate flooding in this 252 catchment, consistent with the mechanism being an increase in the frequency 253 of the zonal regime, and so, successions of strong but fast-moving storms. 254Outputs from CLASSIC are combined with information about the location of
Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 • C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 • C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climatePublished by Copernicus Publications on behalf of the European Geosciences Union. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the second two being estimates of a similar decade but under 1.5 and 2 • C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 • C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 • C scenario.
In the computation of the EC-Earth results, we accidentally included all grid boxes and not only the land points as we intended. We redid the calculations using the land points only. This implies an update to figure 6 (given below), but it makes only a slight differences to the EC-Earth results. The comparison with the observed fit for the model evaluation is somewhat better in the dispersion parameter / and now good in the shape parameter, although the model now requires a bias correction of 18%. The increase in intensity for land points only is ΔI = 17% (11% ... 23%), compared to the ΔI = 17% (10% ... 23%) for all points. The risk ratio is a bit higher, 2.5 (1.8 ... 6.7) instead of the 2.2 (1.5 ... 4.1) reported in the article. Updated synthesis and conclusionsThis changes figure 7 slightly as well, but does not affect the conclusions. The change in increase remains 15% with an uncertainty range 8%-19%. The change in risk ratio stays the same, a factor of three, but with a slightly higher uncertainty range, 1.6-6 rather than 1.5-5. This strengthens our conclusions by a negligible factor.
Abstract. We describe and evaluate the NMMB/BSC-Dust, a new dust aerosol cycle model embedded online within the NCEP Non-hydrostatic Multiscale Model (NMMB). NMMB is a further evolution of the operational Nonhydrostatic Mesoscale Model (WRF-NMM), which together with other upgrades has been extended from meso to global scales. Its unified non-hydrostatic dynamical core is prepared for regional and global simulation domains. The new NMMB/BSC-Dust is intended to provide short to mediumrange weather and dust forecasts from regional to global scales and represents a first step towards the development of a unified chemical-weather model. This paper describes the parameterizations used in the model to simulate the dust cycle including sources, transport, deposition and interaction with radiation. We evaluate monthly and annual means of the global configuration of the model against the AEROCOM dust benchmark dataset for year 2000 including surface concentration, deposition and aerosol optical depth (AOD), and we evaluate the daily AOD variability in a regional domain at high resolution covering Northern Africa, Middle East and Europe against AERONET AOD for year 2006. The NMMB/BSC-Dust provides a good description of the horiCorrespondence to: C. Pérez (carlos.perezga@nasa.gov) zontal distribution and temporal variability of the dust. Daily AOD correlations at the regional scale are around 0.6-0.7 on average without dust data assimilation. At the global scale the model lies within the top range of AEROCOM dust models in terms of performance statistics for surface concentration, deposition and AOD. This paper discusses the current strengths and limitations of the modeling system and points towards future improvements.
[1] More than 2300 observed cloud layers were analyzed to investigate the impact of aged Saharan dust on heterogeneous ice formation. The observations were performed with a polarization/Raman lidar at the European Aerosol Research Lidar Network site of Leipzig, Germany (51.3°N, 12.4°E) from February 1997 to June 2008. The statistical analysis is based on lidar-derived information on cloud phase (liquid water, mixed phase, ice cloud) and cloud top height, cloud top temperature, and vertical profiles of dust mass concentration calculated with the Dust Regional Atmospheric Modeling system. Compared to dust-free air masses, a significantly higher amount of ice-containing clouds (25%-30% more) was observed for cloud top temperatures from −10°C to −20°C in air masses that contained mineral dust. The midlatitude lidar study is compared with our SAMUM lidar study of tropical stratiform clouds at Cape Verde in the winter of 2008. The comparison reveals that heterogeneous ice formation is much stronger over central Europe and starts at higher temperatures than over the tropical station. Possible reasons for the large difference are discussed.
We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change.
Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically-based index of fire weather, the Fire Weather Index, long-term observations of heat and drought, and eleven large ensembles of state-of-the-art climate models. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor 5 two due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season September-February. The observations, however, show a weak drying trend in the annual mean. Finally, we find large trends in the Fire Weather Index in the ERA5 reanalysis, and a smaller but significant increase by at least 30% in the models. The trend is mainly driven by the increase 10 of temperature extremes and hence also likely underestimated. For the 2019/20 season more than half of the July-December drought was driven by record excursions of the Indian Ocean dipole and Southern Annular Mode. These factors are included in the analysis. The study reveals the complexity of the 2019/20 bushfire event, with some, but not all drivers showing an imprint of anthropogenic climate change.
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