Abstract Regional climate models (RCMs) exhibit greater potential than global models (GCMs) in capturing geographical details of climate change arising from orography and land-water distribution, but dynamical downscalings are only available for a limited number of GCMs. The full GCM ensembles are much more representative. Furthermore, the current EURO-CORDEX RCM runs most likely underestimate future warming. Thus, neither GCMs nor RCMs as such constitute an ideal tool for preparing reliable spatially detailed climate projections. This study introduces an easy-to-use GCM-RCM hybrid method that takes advantage of the best properties of both model categories. The large-scale response is adopted from GCM simulations, but the pattern is enriched with RCM-simulated details. For temperature projections, the procedure resembles the conventional pattern-scaling technique, but spatial averages of temperature change used for scaling are calculated over an area surrounding each grid point. For precipitation, a linearised version of the method has been formulated. The applicability of the method is studied by integrating spatial details from 12 EURO-CORDEX RCM simulations with the CMIP6 multi-GCM mean projection. The resulting temperature responses include RCM-generated spatial details of up to ~1°C while effectively correcting the general tendency of RCMs to underestimate warming in Europe. The impact of the RCMs is most significant in summer. For precipitation, geographical details originating from the different CORDEX runs tend to diverge, leading to a low signal-to-noise ratio. This drawback is not due to the hybrid method but the substantial impact of internal variability on small-scale precipitation changes.
<p>We investigate how a regionally confined radiative forcing of South and East Asian aerosols translate into local and remote surface temperature responses across the globe. To do so, we carry out equilibrium climate simulations with and without modern day South and East Asian anthropogenic aerosols in two climate models with independent development histories (ECHAM6.1 and NorESM1). &#160;We run the models with the same anthropogenic aerosol representations via MACv2-SP (a simple plume implementation of the 2<sup>nd</sup> version of the Max Planck Institute Aerosol Climatology). This leads to a near identical change in instantaneous direct and indirect aerosol forcing due to removal of Asian aerosols in the two models. We then robustly decompose and compare the energetic pathways that give rise to the global and regional surface temperature effects in the models by a novel temperature response decomposition method, which translated the changes in atmospheric and surface energy fluxes into surface temperature responses by using a concept of planetary emissivity. &#160;</p><p>We find that the removal of South and East Asian anthropogenic aerosols leads to strong local warming &#160;response from increased clear-sky shortwave radiation over the region, combined with opposing warming and cooling responses due to changes in cloud longwave and shortwave radiation. However, the local warming response is strongly modulated by the changes in horizontal atmospheric energy transport. Atmospheric energy transport and changes in clear-sky longwave radiation redistribute the surface temperature responses efficiently across the Northern hemisphere, and to a lesser extent also over the Southern hemisphere. The model-mean global surface temperature response to Asian anthropogenic aerosol removal is 0.26&#177;0.04 &#176;C (0.22&#177;0.03 for ECHAM6.1 and 0.30&#177;0.03 &#176;C for NorESM1) of warming. Model-to-model differences in global surface temperature response mainly arise from differences in longwave cloud (0.01&#177;0.01 for ECHAM6.1 and 0.05&#177;0.01 &#176;C for NorESM1) and shortwave cloud (0.03&#177;0.03 for ECHAM6.1 and 0.07&#177;0.02 &#176;C for NorESM1) responses. The differences in cloud responses between the models also dominate the differences in regional temperature responses. In both models, the Northern hemispheric surface warming amplifies towards the Arctic, where the total temperature response is highly seasonal and modulated by seasonal changes in oceanic heat exchange and clear-sky longwave radiation.</p><p>We estimate that under a strong Asian aerosol mitigation policy tied with strong greenhouse gas mitigation (Shared Socioeconomic Pathway 1-1.9) the Asian aerosol reductions can add around 8 years&#8217; worth of current day global warming during the next few decades.</p>
Abstract. A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901–2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.
Abstract. Little is known about how the structure of extra-tropical cyclones will change in the future. In this study aquaplanet simulations are performed with a full complexity atmospheric model. These experiments can be considered as an intermediate step towards increasing knowledge of how, and why, extra-tropical cyclones respond to warming. A control simulation and a warm simulation in which the sea surface temperatures are increased uniformly by 4 K are run for 11 years. Extra-tropical cyclones are tracked, cyclone composites created, and the omega equation applied to assess causes of changes in vertical motion. Warming leads to a 3.3 % decrease in the number of extra-tropical cyclones, no change to the median intensity nor life time of extra-tropical cyclones, but to a broadening of the intensity distribution resulting in both more stronger and more weaker storms. Composites of the strongest extra-tropical cyclones show that total column water vapour increases everywhere relative to the cyclone centre and that precipitation increases by up to 50 % with the 4 K warming. The spatial structure of the composite cyclone changes with warming: the 900–700-hPa layer averaged potential vorticity, 700-hPa ascent and precipitation maximums associated with the warm front all move polewards and downstream and the area of ascent expands in the downstream direction. Increases in ascent forced by diabatic heating and thermal advection are responsible for the displacement whereas increases in ascent due to vorticity advection lead to the downstream expansion. Finally, maximum values of ascent due to vorticity advection and thermal advection weaken slightly with warming whereas those attributed to diabatic heating increase. Thus, cyclones in warmer climates are more diabatically driven.
The authors of this study recently proposed a resampling method for deriving probabilistic forecasts of near-term climate change and presented some results focusing on temperature and precipitation changes in southern Finland from 1971–2000 to 2011–2020. Here, the sensitivity of the resulting forecasts to two details of the methodology is studied. First, to account for differences between simulated and observed climate variability, a variance correction technique is devised. Second, the sensitivity of the forecasts to the choice of the baseline period is studied. In southern Finland, the variance correction technique generally widens the derived probability distributions of precipitation change, mirroring an underestimate of the observed precipitation variability in climate models. However, the impact on the derived probability distributions of temperature change is small. The choice of the baseline period is generally more important, but again the forecasts of temperature change are less sensitive to different options than those of precipitation change. Crossverification suggests that the variance correction leads to a slight improvement in the potential quality of the probabilistic forecasts, especially for precipitation change. The optimal baseline length appears to be at least 30 yr, and the baseline should be as late as possible (e.g. 1971–2000 is preferable over 1961–1990).
CO2-induced changes in the interannual variability of monthly surface air temperature and precipitation are studied using 19 model experiments participating in the second phase of the Coupled Model Intercomparison Project (CMIP2). The magnitude of variability in the control runs appears generally reasonable, but it varies a great deal between different models, almost all of which overestimate temperature variability on low-latitude land areas. In most models the gradual doubling of CO2 leads to a decrease in temperature variability in the winter half-year in the extratropical Northern Hemisphere and over the high-latitude Southern Ocean. Over land in low latitudes and in northern midlatitudes in summer, a slight tendency toward increased temperature variability occurs. The standard deviation of monthly precipitation increases, on average, where the mean precipitation increases but also does so in some areas where the mean precipitation decreases slightly. The coefficient of variation of precipitation (i.e., the ratio between the standard deviation and the mean) also tends to increase in most areas, especially where the mean precipitation decreases. However, the changes in variability are less similar between the 19 experiments than the changes in mean temperature and precipitation, at least partly because they have a much lower signal-to-noise ratio. In addition, the changes in the standard deviation of monthly temperature are generally much smaller than the time-mean warming, which suggests that future changes in the extremes of interannual temperature variability will be largely determined by the latter.
Abstract In year 2006, Räisänen and Ruokolainen proposed a resampling ensemble technique for probabilistic forecasts of near-term climate change. Here, the resulting forecasts of temperature and precipitation change from years 1971–2000 to 2011–2020 are verified. The forecasts of temperature change are found to be encouraginly reliable, with just 9% and 10% of the local annual and monthly mean changes falling outside the 5–95% forecast range. The verification statistics for temperature change represent a large improvement over the statistics for a surrogate no-forced-change forecast, and they are largely insensitive to the observational data used. The improvement for precipitation changes is much smaller, to a large extent due to the much lower signal-to-noise ratio of precipitation than temperature changes. In addition, uncertainty in observations is a major complication in verification of precipitation changes. For the main source of precipitation data chosen in the study, 20% and 15% of the local annual and monthly mean precipitation changes fall outside the 5–95% forecast range.
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