Climate processes

Abstract:

<jats:title>Abstract</jats:title><jats:p>The increase in global-mean precipitation with global-mean temperature (hydrological sensitivity; <jats:inline-formula><jats:alternatives><jats:tex-math>$$\eta$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>η</mml:mi> </mml:math></jats:alternatives></jats:inline-formula>) is constrained by the atmospheric energy budget, but its magnitude remains uncertain. Here we apply warming patch experiments to a climate model to demonstrate that the spatial pattern of sea surface warming can explain a wide range of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\eta$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>η</mml:mi> </mml:math></jats:alternatives></jats:inline-formula>. Warming in tropical strongly ascending regions produces <jats:inline-formula><jats:alternatives><jats:tex-math>$$\eta$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>η</mml:mi> </mml:math></jats:alternatives></jats:inline-formula> values even larger than suggested by the Clausius–Clapeyron relationship (7% K<jats:sup>−1</jats:sup>), as the warming and moisture increases can propagate vertically and be transported globally through atmospheric dynamics. Differences in warming patterns are as important as different treatments of atmospheric physics in determining the spread of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\eta$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>η</mml:mi> </mml:math></jats:alternatives></jats:inline-formula> in climate models. By accounting for the pattern effect, the global-mean precipitation over the past decades can be well reconstructed in terms of both magnitude and variability, indicating the vital role of the pattern effect in estimating future intensification of the hydrological cycle.</jats:p>

Abstract:

Despite their small size, aerosols strongly influence Earth's climate. Aerosols scatter and absorb radiation referred to as aerosol-radiation interactions but also modify the properties of clouds, as cloud droplets form on aerosol particles, referred to as aerosol-cloud interactions. Kilometer-scale simulations allow us to examine long-standing questions related to these interactions. Such simulations resolve atmospheric motions on scales of a few kilometers and represent important atmospheric processes like convective updrafts that were parameterized previously. Regional simulations revealed significant effects of aerosols on convective clouds and provided insights into the underlying processes and drivers.&#160;To examine these interactions with the climate model ICON, we developed the simple aerosol model HAMlite based on and fully traceable to the complex aerosol model HAM. HAMlite represents aerosols as an ensemble of log-normal modes. To reduce the computational and physical complexity, aerosol microphysics are discarded and aerosol sizes and compositions are prescribed. The selection of modes is flexible and can include the Aitken, accumulation, and coarse modes. The calculation of aerosol properties and thermodynamics remains fully consistent with HAM. HAMlite is linked to the atmospheric processes of ICON. Aerosols are transported as tracers in the dynamical core and coupled to the radiation, turbulence, and cloud microphysics schemes.We present first results from global simulations with ICON-HAMlite. The atmosphere is governed by non-hydrostatic conservation equations, the land is represented with the dynamic vegetation model JSBACH, and the sea surface temperature and sea ice are prescribed with the AMIP database. The horizontal resolution is about 5 km and time period is about 40 days. First, we evaluate the global distributions of the different aerosol modes. And second, we investigate how aerosols influence the diurnal cycle and deep convection in the tropics. In contrast to regional simulations, global simulations include the large-scale circulation and in particular the budgetary constraints on precipitation due to the conservation of water and energy.

Abstract:

Aerosol-cloud interactions remain a large source of uncertainty in anthropogenic climate forcing. One of the reasons for this uncertainty is the confounding role of meteorology, influencing both aerosols and cloud properties. To untangle these variables, ship tracks, the clouds polluted by shipping emissions, have been widely studied. Recently, the use of shipping emissions locations and amounts, combined with reanalysis winds, has allowed us to study polluted clouds by following ship emissions to the locations they are advected to by the time of a satellite measurement of clouds. This is possible even when no visible tracks appear in satellite images. Here, we additionally use emission amounts data and investigate their effect on key cloud characteristics like droplet numbers and liquid water. This per-ship emissions data is valuable as it allows us to investigate cloud property changes stratified by region or meteorology. Between the ships with the lowest and highest emissions, droplet number anomalies increase by an order of magnitude from 0.25% to 2.5%, but the effect saturates at high emissions. We furthermore present evidence that increases of liquid water are insensitive to the amount of aerosol increases. Crossing data with a set of machine-learning detected ship tracks, we show that emissions amount has a similarly saturating effect on the formation of visible tracks as on droplet number, increasing roughly linearly for a large range of emissions before saturating (and even declining) at high emissions. The saturation of cloud responses at relatively high emissions could indicate that clouds react strongly to reductions in aerosol emissions.&#160;

Abstract:

In this contribution, a statistical learning technique is used to quantify the response of cloud radiative effects to changes in a large number of environmental factors in spatial observation data.Clouds play a key role for the Earth&#8217;s energy balance; however, their response to climatic and anthropogenic aerosol emission changes is not clear, yet. Here, 20 years of satellite observations of cloud radiative effects (CRE) are analysed together with reanalysis data sets in a (regularised) ridge regression framework to quantitatively link the variability of observed CREs to changes in environmental factors, or cloud-controlling factors (CCFs). In the literature the meteorological kernels of such CCF analyses are typically established in regime-specific regression frameworks based on a low (2-8) number of CCFs. In our data-driven approach, the capabilities of the regularised regression to deal with collinearities in a large number of predictors are exploited to establish a regime-independent CCF framework based on a large number of CCFs. Using a reference 7-CCF framework, we show that ridge regression produces nearly identical patterns of CCF sensitivities when compared to the traditional regression. In the data-driven framework, however, the traditional regression fails at producing consistent results due to overfitting. The data-driven analysis reveals distinct regional patterns of CCF importance for shortwave and longwave CRE:&#160;Sea surface temperatures and inversion strength are important for shortwave CRE in stratocumulus regions, in agreement with existing studies. However, zonal wind speeds in the free troposphere and surface fluxes are also shown to be important. Free tropospheric meridional winds are important drivers of CRE in the subtropical belts (20&#176;-40&#176;) in both hemispheres, likely capturing aspects of Rossby Wave-related CRE variability.&#160; Aerosols are shown to be most important for shortwave CRE in the regions of stratocumulus to cumulus transition.&#160; While the multivariate method aims at limiting the influence of confounding factors on the estimated sensitivities, particularly the aerosol-CRE sensitivity may still be confounded to a degree. Future analyses of interactions between different CCFs and comparisons to global climate models are outlined.