C2SM Newsletter vol. 35

News from EXCLAIM

The .

The role of atmospheric dynamics and large-scale topography in driving heatwaves

Heatwaves are weather events characterized by extreme near-surface temperature anomalies that persist for several days, and therefore lead to catastrophic impacts on natural ecosystems, agriculture, human health, and economies. Different physical processes can contribute to the temperature anomaly associated with heatwaves. Previous studies have shown that increased solar radiation and adiabatic heating associated with blocking systems and local land–atmosphere coupling are important drivers of summer heatwaves. Less is known about the fundamental role of atmospheric large-scale dynamics and topography in generating heatwaves. Here, we perform idealized model simulations where all physical parameterisations are substituted by a simple zonally symmetric temperature relaxation scheme. This allows us to characterize the dynamical processes involved in the life cycle of heatwaves occurring at different latitudes. We find that blocking plays an active role in the life cycle of high- and midlatitude heatwaves, while blocking is less relevant for low-latitude heatwaves. Rossby-wave packets are the dominant drivers for midlatitude heatwaves, with horizontal advection being the main mechanism leading to heat extremes. Heatwaves exhibit a higher persistence and frequency near the pole and Equator compared with the midlatitudes, but a higher intensity in the midlatitudes compared with higher and lower latitudes. Topography located along the midlatitude jet has the largest impact on the heatwave distribution around the planet, resulting in increased heatwave frequency upstream for moderate topographic forcing and a circumglobal increase for topographic elevations above 6 km. Identifying the most relevant processes driving heatwaves can potentially benefit the prediction and representation of extreme events in operational weather and climate forecast systems.

Jiménez-​Esteve, B. and Domeisen, D.I.V. (2022):, The role of atmospheric dynamics and large-​scale topography in driving heatwaves, Q. J. R. Meteorol. Soc., external pagehttps://doi.org/10.1002/qj.4306call_made

COSMO simulations of ice clouds in the West African monsoon

This study investigates the formation of ice clouds related to deep convection in the West African monsoon, using stable water isotopes as tracers of moist processes. In the convection-permitting simulations, performed with the COSMOiso model, the following five key processes related to tropical ice clouds can be distinguished based on isotope information (see figure): (1) convective lofting of enriched ice into the upper troposphere, (2) cirrus clouds that form in situ from ambient vapour under equilibrium fractionation, (3) sedimentation and sublimation of ice in the mixed-phase cloud layer, (4) sublimation of ice in convective downdraughts, and (5) the freezing of liquid water just above the 0◦C isotherm in convective updraughts. This study demonstrates that isotopes are useful to disentangle the role of different processes in the West African monsoon water cycle and their impact on the tropical tropopause layer. The study has been selected as a highlight paper in ACP.

De Vries, A. J., F. Aemisegger, S. Pfahl, and H. Wernli, (2022): Stable water isotope signals in tropical ice clouds in the West African monsoon simulated with a regional convection-permitting model. Atmos. Chem. Phys., 22, 8863–8895. external pagehttps://doi.org/10.5194/acp-22-8863-2022call_made

Lagrangian Particle Dispersion Models in the Grey Zone of Turbulence: Adaptations to FLEXPART-COSMO for Simulations at 1 km Grid Resolution

For the last decade, the atmospheric transport model FLEXPART-COSMO, run at 7 km horizontal resolution, formed the backbone of numerous studies on greenhouse gas emissions. However, when using higher resolution COSMO (1 km resolution) to drive FLEXPART, transport became too dispersive. This study demonstrates that a double representation of turbulent transport (partly resolved by COSMO grid-scale winds, and also parameterised as a stochastic process in FLEXPART) is responsible for additional dispersion. We suggest an alternative turbulence parameterisation for FLEXPART, which aligns the turbulent kinetic energy between COSMO and FLEXPART. The performance of FLEXPART using the new parameterisation is validated against observations of a power plant plume and long-term methane observations at a tall tower site. The new parameterisation clearly improves model performance in both cases. Future inverse modelling of Swiss greenhouse gas emissions is expected to largely benefit from these model developments.

Katharopoulos, I., Brunner, D., Emmenegger, L., Leuenberger, M., and Henne, S (2022): Lagrangian Particle Dispersion Models in the Grey Zone of Turbulence: Adaptations to FLEXPART-COSMO for Simulations at 1 km Grid Resolution, Bound. Lay. Met., doi: external page10.1007/s10546-022-00728-3call_made

An ensemble-based statistical methodology to detect differences in weather and climate model executables

Many changes in weather and climate models are not meant to change the model behavior (i.e., code rearrangements or changes in hardware/software infrastructure). However, verifying such changes is challenged by the chaotic nature of our atmosphere, where even rounding errors can significantly impact individual simulations.
We propose a new methodology for quantifying and verifying the impacts of such changes by using ensemble simulations in combination with a statistical hypothesis test at the grid-cell level.
The methodology shows great sensitivity and can detect changes such as a tiny amount of added diffusion or the switch from double to single precision in the COSMO model. However, the test showed no differences after a major system update of Piz Daint or between the GPU and CPU versions of COSMO.
Our results suggest that short-term ensemble simulations are best suited for the methodology, making the test computationally inexpensive and thus ideal for automated testing.

Zeman, C. and Schär, C (2022).: An ensemble-based statistical methodology to detect differences in weather and climate model executables, Geosci. Model Dev., 15, 3183–3203, external pagehttps://doi.org/10.5194/gmd-15-3183-2022call_madeexternal pagecall_made

21st Century Alpine Climate Change

A new paper published in Climate Dynamics exploits the EURO-CORDEX RCM ensemble (the same ensemble as used for CH2018) to quantify projected future climate change on an Alpine scale. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. Among other indicators, surface snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings.

Kotlarski S, Gobiet A, Morin S, Olefs M, Rajczak J, Samacoits R (2022): 21st Century Alpine Climate Change. Climate Dynamics, external pagehttps://doi.org/10.1007/s00382-022-06303-3.call_madeexternal pagecall_madeexternal pagecall_made

Internal variability and decadal-scale trends of surface solar radiation

The climate science community has since long pinpointed the importance of understanding unforced climate variability for a better evaluation of climate projections. Internal variability (a.k.a. noise) arises solely from processes intrinsic to the climate system and is amplified by the non-linear interactions between them. One of the reasons for running forced simulations with many ensemble members is to test the models' response with different background states of the noise. This helps to better distinguish their response to external forcings from noise. Yet it still does not enable a direct one-to-one comparison with observations, because we cannot really simulate all components of the background state in their correct phases and amplitudes at the correct times. We take an alternative, statistical approach to gain a better understanding and quantitative estimate of the influence of internal variability on decadal-scale trends of surface solar radiation (SSR), a key energy budget variable. We performed a statistical analysis on unforced multi-century control simulations from the Coupled Models Intercomparison Project – Phase 6 (CMIP6), which do not include any natural (e.g., volcanoes) or anthropogenic (e.g., greenhouse gases and aerosols) forcings. We show that for annual mean SSR the distribution of all possible N-year trends can be analytically derived from the standard deviation of the underlying time series (σts). This results from two important properties of the annual mean SSR time series: that the distribution of all values is Gaussian and that there is no autocorrelation in time. Based on our analysis, we can estimate the probability of an unforced trend (or the unforced component contributing to a trend) for any location on the planet. The figure shows the simulated trend distribution over different N-year periods for Lindenberg, Germany. At this location, an unforced trend with a magnitude of 2.7 Wm-2/decade can be sustained over 30-years with a probability of 10% (90th percentile of the 30-year trends distribution). The magnitude of unforced trends decreases over longer time scales or when spatially averaged.

Chtirkova, B., Folini, D., Correa, L. F., & Wild, M. (2022). Internal variability of all-sky and clear-sky surface solar radiation on decadal timescales. Journal of Geophysical Research: Atmospheres, 127, e2021JD036332. external pagehttps://doi.org/10.1029/2021JD036332call_madeexternal pagecall_madeexternal pagecall_madeexternal pagecall_made

Aerosol’s fingerprint on clouds: Increases in cloud cover appear to dominate the cooling effect

Aerosol’s fingerprint on clouds remains the largest uncertainty for climate forcing since decades. The impacts derived from climate models are poorly constrained by observations because retrieving robust large-scale signals of aerosol–cloud interactions is frequently hampered by the considerable noise associated with meteorological co-variability.
To solve this, we pioneer a new novel approach to distinguish the aerosol’s fingerprint in clouds from natural variability using an unprecedented effusive volcanic eruption in Iceland in 2014 as a natural experiment. This approach combines modern machine-learning data-science technique and long-term satellite remote sensing records, to generate an emulator which tells us how clouds would look like in the absence of aerosol perturbation.
Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a notably smaller radiative impact than changes in cloud fraction.

Chen, Y., Haywood, J., Wang, Y., Malavelle, F., Jordan, G., Partridge, D., Fieldsend, J., De Leeuw, J., Schmidt, A., Cho, N., Oreopoulos, L., Platnick, S., Grosvenor, D., Field, P., and Lohmann, U (2022): Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover, Nature Geoscience, external pagehttps://doi.org/10.1038/s41561-022-00991-6call_madeexternal pagecall_madeexternal pagecall_madeexternal pagecall_madeexternal pagecall_made