It is widely recognized that both natural and anthropogenic aerosols are crucial in the radiative balance and that the direct effect in cloud-free regions can be substantial and impact variables such as surface temperature and winds (e.g. Yu et al., 2006; Bellouin et al., 2005). The aerosol direct effect consists of the sum of two phenomena: scattering/absorption of incoming solar radiation and absorption/emission of long-wave radiation. Aerosols also serve as cloud condensation nuclei and affect cloud properties such as the cloud life cycle, the optical properties and the precipitation activity of clouds (indirect effect). Aerosol particles also impact air quality and represent a serious public health issue, as shown by recent particulate matter (PM) pollution events in western Europe and China (Wang 2014; Sun 2014).
The role of aerosols in the Earth's radiation balance has been investigated by the climate modelling community since the early 1990s (e.g. Crutzen, 1990; Coakley, 1992; Hansen, 1992). However, in Numerical Weather Prediction, state-of-the-art operational models are still using climatology which can describe the average effect of aerosols on the radiative balance. This is due to the fact that there has been so far no definite proof that sophisticated aerosol schemes are needed and the cost of running a full integrated aerosol system at the high resolution of current NWP systems is considered at the moment prohibitive. In order to assess the impact of prognostics aerosols on NWP forecasts, the Working Group on Numerical Experimentation (WGNE) coordinated a set of experiments. Results (Remy et al., 2015) showed that the impact of the dust aerosols on surface temperature and winds was noticeable, even though the synoptic situation was not much affected by the radiative forcing of the prognostic aerosols. Similar conclusions were also reached by (Colarco, 2014).
As for NWP, all the operational S2S models contributing to the S2S database use climatological aerosols. However, recent studies suggest a strong modulation of aerosols by the Madden Julian Oscillation which is a major source of predictability in the tropics at the sub-seasonal to seasonal time range (e.g. Figure 5) and that the MJO-related intra-seasonal variance accounts for about 25% of the total AOT variance over the tropical Atlantic (Tian et al., 2011) primarily through its influence on the Atlantic low-level zonal winds (Figure 6). Guo et al. (2013) indicated that dust in this region is strongly influenced by the MJO-modulated trade wind and precipitation anomalies, with anomalies lasting as long as one MJO phase, whereas smoke is less affected.
Figure 5. Composite maps of the TOMS Aerosol Index anomalies (colour shading) associated with the MJO indicated by the CMAP rainfall anomalies (contours). TOMS AI anomalies are only plotted if they exceed 95% confidence limit using a Student’s t-test. Lags are in pentads. Note that the correlation between precipitation and TOMS AI tends to be negative. A similar diagram using MODIS Aerosol Optical Thickness (AOT) shows a weak positive correlation. See Tian et al. (2008) for discussion.
These studies suggest that the MJO can represent an important source of predictability for aerosols at the sub-seasonal to seasonal timescale which is not represented in current operational S2S systems which make use of climatological aerosols. This impact of the MJO on the aerosol distribution is likely to feed back on the atmospheric circulation. The use of climatological aerosols instead of prognostic aerosols may be an important gap in S2S forecasting system. Besides the potential feedbacks of natural and anthropogenic aerosols on the atmospheric dynamics and the skill of the forecast on sub-seasonal scales, aerosols often have serious impacts on air quality and human health. Skilful S2S forecasts of air quality could be of great socioeconomic benefit, e.g. over SE Asia associated with forest fires, in the meningitis belt of the Sahel, and South Asian cities.
Figure 6. (Upper 2 panels) Composite maps of boreal winter MJO‐related MODIS AOT anomalies (multiplied by 100) (coloured areas) and TRMM 3B42 precipitation anomalies (mm day -1) (contours) over the tropical Atlantic region for MJO phases 2 and 6 as defined in Tian et al. (2011). Only AOT anomalies above a 90% confidence limit are shown. Solid contours indicate positive precipitation anomalies, while dashed contours indicate negative precipitation anomalies at a 0.5 mm day−1 interval. (Lower 2 panels), same except for NCEP/NCAR low‐level (averaged from 925 hPa to 700 hPa) zonal wind anomalies (m s−1).
The main goal of this sub-project will be to assess the benefits of using prognostic aerosols rather than the climatology used in the current operational S2S models. The main scientific questions which will be addressed include:
- What is the impact of prognostic (vs climatologically specified) aerosol loading in the atmosphere on S2S forecasts via its effects on radiation? This will include assessing the impact of prognostic aerosols on model systematic errors and on model probabilistic forecast skill scores. This would build on the case study coordinated by WGNE (see description in the previous section), although the approach here could be more systematic than case studies.
- What level of complexity is needed? As mentioned above, the cost of current aerosol models, which include often dozens of species is prohibitive. For instance, at ECMWF, the inclusion of a prognostic aerosols scheme with only 8 species would increase the cost of the S2S forecasts by about 50%. Therefore, it would be important to determine which species play the most important role at the S2S timescale and which ones can be neglected. This would help design more affordable aerosol models for S2S forecasts. This would also include assessing the number of source regions and scavenging mechanisms needed in the simulation
- What is the predictability of aerosols (e.g. dust) at the S2S timescale, and what would be the value of these forecasts for applications? This study will include assessing the ability of S2S models to simulate the modulation of aerosols by the MJO and other sources of sub-seasonal to seasonal predictability. A main motivation of this sub-project is to assess the impact of prognostic aerosols on the S2S forecasts, but the sub-seasonal to seasonal prediction of the aerosol concentration itself can represent a very valuable source of information for applications such as meningitis and air quality. This study would link with the WMO SDS-WAS, which has performed a similar study but for short and medium-range forecasting. It would also link with groups running off-line aerosol transport models, to explore using forecast/re-forecast data in the S2S database to drive these models offline. These models typically need high-resolution 6-hourly fields, but downscaling approaches may be feasible.
This sub-project will explore the possibility of performing coordinated experiments to address the above questions. In these experiments, the full scope of an “active” simulation would include:
- Generation of aerosol abundance in the atmosphere from source regions (e.g. wind blowing over deserts, sea salt - more from storm regions)
- Precipitation scavenging and general gravitation fall out
- Advection in between the above two
- And then its impact is only on radiation
Currently few operational and research centres are currently able to produce S2S simulations with "active aerosols” (defined here as prognostics aerosol loading for the purpose of radiation impacts - not cloud-aerosol interactions). A first step would to contact the centres who participated to the WGNE case study experiment to assess if it is possible to use these models for extended-range forecast experiments.