Future summer warming pattern under climate change is affected by lapse-rate changes

Greenhouse-gas-driven global temperature change projections exhibit spatial variations, meaning that certain land areas will experience substantially enhanced or reduced surface warming. It is vital to understand enhanced regional warming anomalies as they locally increase heat-related risks to human health and ecosystems. We argue that tropospheric lapse-rate changes play a key role in shaping the future summer warming pattern around the globe in mid-latitudes and the tropics. We present multiple lines of evidence supporting this finding based on idealized simulations over Europe, as well as regional and global climate model ensembles. All simulations consistently show that the vertical distribution of tropospheric summer warming is different in regions characterized by enhanced or reduced surface warming. Enhanced warming is projected where lapse-rate changes are small, implying that the surface and the upper troposphere experience similar warming. On the other hand, strong lapse-rate changes cause a concentration of warming in the upper troposphere and reduced warming near the surface. The varying magnitude of lapse-rate changes is governed by the temperature dependence of the moist-adiabatic lapse rate and the available tropospheric humidity. We conclude that tropospheric temperature changes should be considered along with surface processes when assessing the causes of surface warming patterns.publishedVersio

The pseudo-global-warming (PGW) approach: Methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses

The term “pseudo-global warming” (PGW) refers to a simulation strategy in regional climate modeling. The strategy consists of directly imposing large-scale changes in the climate system on a control regional climate simulation (usually representing current conditions) by modifying the boundary conditions. This differs from the traditional dynamic downscaling technique where output from a global climate model (GCM) is used to drive regional climate models (RCMs). The PGW climate changes are usually derived from a transient global climate model (GCM) simulation. The PGW approach offers several benefits, such as lowering computational requirements, flexibility in the simulation design, and avoiding biases from global climate models. However, implementing a PGW simulation is non-trivial, and care must be taken not to deteriorate the physics of the regional climate model when modifying the boundary conditions. To simplify the preparation of PGW simulations, we present a detailed description of the methodology and provide the companion software PGW4ERA5 facilitating the preparation of PGW simulations. In describing the methodology, particular attention is devoted to the adjustment of the pressure and geopotential fields. Such an adjustment is required when ensuring consistency between thermodynamical (temperature and humidity) changes on the one hand and dynamical changes on the other hand. It is demonstrated that this adjustment is important in the extratropics and highly essential in tropical and subtropical regions. We show that climate projections of PGW simulations prepared using the presented methodology are closely comparable to traditional dynamic downscaling for most climatological variables.publishedVersio

Regional climate model projections underestimate future warming due to missing plant physiological CO 2 response

Many countries rely on regional climate model (RCM) projections to quantify the impacts of climate change and to design their adaptation plans accordingly. In several European regions, RCMs project a smaller temperature increase than global climate models (GCMs), which is hypothesised to be due to discrepant representations of topography, cloud processes, or aerosol forcing in RCMs and GCMs. Additionally, RCMs do generally not consider the vegetation response to elevated atmospheric CO2 concentrations; a process which is, however, included in most GCMs. Plants adapt to higher CO2 concentrations by closing their stomata, which can lead to reduced transpiration with concomitant surface warming, in particular, during temperature extremes. Here we show that embedding plant physiological responses to elevated CO2 concentrations in an RCM leads to significantly higher projected extreme temperatures in Europe. Annual maximum temperatures rise additionally by about 0.6 K (0.1 K in southern, 1.2 K in northern Europe) by 2070–2099, explaining about 67% of the stronger annual maximum temperature increase in GCMs compared to RCMs. Missing plant physiological CO2 responses thus strongly contribute to the underestimation of temperature trends in RCMs. The need for robust climate change assessments calls for a comprehensive implementation of this process in RCM land surface schemes

CH2018 - National climate scenarios for Switzerland : how to construct consistent multi-model projections from ensembles of opportunity

The latest Swiss Climate Scenarios (CH2018), released in November 2018, consist of several datasets derived through various methods that provide robust and relevant information on climate change in Switzerland. The scenarios build upon the regional climate model projections for Europe produced through the internationally coordinated downscaling effort EURO-CORDEX. The simulations from EURO-CORDEX consist of simulations at two spatial horizontal resolutions, several global climate models, and three different emission scenarios. Even with this unique dataset of regional climate scenarios, a number of practical challenges regarding a consistent interpretation of the model ensemble arise. Here we present the methodological chain employed in CH2018 in order to generate a multi-model ensemble that is consistent across scenarios and is used as a basis for deriving the CH2018 products. The different steps involve a thorough evaluation of the full EURO-CORDEX model ensemble, the removal of doubtful and potentially erroneous simulations, a time-shift approach to account for an equal number of simulations for each emission scenario, and the multi-model combination of simulations with different spatial resolutions. Each component of this cascade of processing steps is associated with an uncertainty that eventually contributes to the overall scientific uncertainty of the derived scenario products. We present a comparison and an assessment of the uncertainties from these individual effects and relate them to probabilistic projections. It is shown that the CH2018 scenarios are generally supported by the results from other sources. Thus, the CH2018 scenarios currently provide the best available dataset of future climate change estimates in Switzerland

COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review

In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain

High-resolution ensemble forecasts of a polar low by non-hydrostatic downscaling

A short range, limited area ensemble prediction system, LAMEPS, is currently in operational use at the Norwegian Meteorological Institute. It employs 3D-Var for 6 hourly data assimilation cycling for analysis of the control forecast. Initial time and lateral boundaries ensemble perturbations are computed from the 20 + 1 member TEPS (targeted EPS at ECMWF). LAMEPS is run with the quasi-hydrostatic model HIRLAM version 7.1.4 on a 12 km horizontal grid mesh. In this study we have downscaled each LAMEPS member with the non-hydrostatic UK Met Office Unified Model (UM) version 6.1 in order to study the predictability and the predictions of extreme weather related to a polar low observed in the Barents and Norwegian Seas between 3 and 4 March 2008. This event was extensively covered by the observation campaign of the IPY-THORPEX project. UM is in this study configured with 4 km horizontal grid mesh. The domain size has been investigated by using two different domains, one with 390 x 490 and one with 300 x 300 grid points. Furthermore, the sensitivity to the physical parameterization in the stable boundary layer has also been explored. Regular observation data, satellite data, and IPY-THORPEX campaign data have been used to compare with the ensemble forecasts. Probabilities of different meteorological parameters and occurrence of extreme weather events have been studied along with ensemble means, ensemble spread and control runs. In addition, two new model diagnostics for comparing against observation data have been developed. These are cloud top temperatures and tracking of the polar lows path. The ensemble forecast shows clear improvements by increasing horizontal resolution with non-hydrostatic dynamics. However, the size of the integration domain affects the prediction substantially. The improvements are greatest for the large domain. The forecasts are also sensitive to the physical parameterization. The experiments with less vertical mixing in the stable boundary layer reduce the area of high probability for the large domain. The results of the tracking algorithm, which finds the strongest mesoscale track in each ensemble member, show that the location of the strongest track depends on domain size and the perturbation of the physics

The dynamic and thermodynamic structure of monsoon low-pressure systems during extreme rainfall events

A cyclone-tracking algorithm is used to identify monsoon low-pressure systems (LPS) in the ERA-interim re-analysis (1979–2010). The LPS that are connected to observed extreme rainfall events are picked out and studied with a focus on their dynamic and thermodynamic structure. Cyclone composite clearly shows the general structure of the LPS, with a pronounced cold core at lower levels and warm core aloft. Evaporative cooling from the falling precipitation is proposed to generate the cold core. The temperature gradients across the cyclone centre are strongest in the early phase of the low. We suggest the baroclinic instability to be important in the development phase of the LPS, whereas the upward motion ahead of the low is maintained through latent heat release in the mature phase. This cooperation between the large-scale flow and the cumulus convection is known as the conditional instability of second kind (CISK). From the composites of the time steps where the extreme precipitation is occurring, a colocation of the strong updraft and vertical velocity is shown. Based on this, we suggest the extreme rainfall events to be a result of the LPS dynamics, which is dominated by the CISK mechanism at this stage of the low. Correlation and co-variability between the LPS precipitation and different meteorological parameters are performed, and we find the LPS precipitation to show a large sensitivity to variability in the vertical velocity and specific humidity at 750 hPa

Monsoon low-pressure systems - the precipitation response to atmospheric warming

The monsoon low-pressure systems (LPS) are amongst the most rain bearing synoptic scale systems that develop during the Indian monsoon, and they play a key role in generating extreme rainfall events in central India. This thesis consists of three scientific papers, which are devoted to the monsoon LPS, with focus on the precipitation associated with the systems. The main motivation for this thesis is to understand how the LPS precipitation may change in a warming world. To examine this issue, a dataset of LPS developing during the Indian monsoon was generated, described in Paper I. A well know tracking algorithm was used to detect the LPS in the ERA-Interim reanalysis during the time period 1979-2010. The LPS that are connected to observed extreme rainfall events are selected, which resulted in a dataset of 39 LPS. Next, high-resolution, convection-permitting, climate sensitivity simulations were performed on 10 cases chosen from the LPS dataset. Control runs are simulated with unperturbed ERA-Interim initial and lateral boundary conditions (LBC). Perturbed runs follow a surrogate climate change approach, in which a uniform temperature perturbation is specified to the LBC but the large-scale flow and relative humidity is unchanged. The difference between the control and perturbed simulations are therefore mainly due to the imposed warming and moisture changes as well as feedbacks to the synoptic scale flow. The change in the mean precipitation following the LPS is described in Paper II, and Paper III focus on the change in the short-duration extreme precipitation released over central India and the runoff response. The results clearly show that in a warmer and more humid atmosphere, the LPS can produce more precipitation, precipitate with a higher precipitation rate and also bring precipitation further into the Indian continent. Based on these results we conclude that there may be an increased risk of more severe flood events in central India. The more than 2 x Clausius-Clapeyron scaling response in the precipitation is explained by the imposed specific humidity increase, a dynamic feedback giving stronger upward motions and a thermodynamic feedback decreasing the atmospheric stability. In the warmer runs the LPS are more intense with a higher propagation speed. The intensification of the storms seems to be interpretable in terms of the conditional instability of second kind mechanism: condensational heating increases along with lowlevel convergence and vertical velocity in response to temperature warming and moisture increases, and as a result the surface low deepens. The results presented show that the precipitation associated with LPS are very sensitive to an increase in the atmospheric temperature and subsequent moisture increase. Changes in the atmospheric moisture content as a result of temperature warming are together with dynamical and thermodynamical feedbacks, affecting both the precipitation intensity and frequency

Causes of future Mediterranean precipitation decline depend on the season

Future mean precipitation in the Mediterranean is projected to decrease year-round in response to global warming, threatening to aggravate water stress in the region, which can cause social and economic difficulties. We investigate possible causes of the Mediterranean drying in regional climate simulations. To test the influence of multiple large-scale drivers on the drying, we sequentially add them to the simulations. We find that the causes of the Mediterranean drying depend on the season. The summer drying results from the land-ocean warming contrast, and from lapse-rate and other thermodynamic changes, but only weakly depends on circulation changes. In contrast, to reproduce the simulated Mediterranean winter drying, additional changes in the circulation and atmospheric state have to be represented in the simulations. Since land-ocean contrast, thermodynamic and lapse-rate changes are more robust in climate simulations than circulation changes, the uncertainty associated with the projected drying should be considered smaller in summer than in winter

Systematic Calibration of a Convection-Resolving Model: Application Over Tropical Atlantic

Non-hydrostatic km-scale weather and climate models show significant improvements in simulating clouds and precipitation, especially of convective nature. However, even km-scale models need to parameterize some physical processes and are thus subject to the corresponding parameter uncertainty. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate-change scenario simulations. In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters' sensitivities are evaluated with respect to a set of validation fields. Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows a methodology previously used for regional climate simulations. This includes simulations considering the interaction of all pairs of parameters, and the exploitation of a quadratic-form metamodel to emulate the simulations. In the current set-up with 5 parameters, 51 simulations are required to build the metamodel. The model is calibrated for the year 2016 and validated in two different years using slightly different model setups (domain and resolution). Both years demonstrate significant improvements, in particular for outgoing shortwave radiation, with reductions of the bias by a factor of 3–4. The results thus show that parameter calibration is a useful and efficient tool for model improvement. Calibrating over a larger domain might help to further improve the overall performance, but could potentially also lead to compromises among different regions and variables, and require more computational resources.publishedVersio