Responsibility of major emitters for country-level warming and extreme hot years

The contributions of single greenhouse gas emitters to country-level climate change are generally not disentangled, despite their relevance for climate policy and litigation. Here, we quantify the contributions of the five largest emitters (China, US, EU-27, India, and Russia) to projected 2030 country-level warming and extreme hot years with respect to pre-industrial climate using an innovative suite of Earth System Model emulators. We find that under current pledges, their cumulated 1991–2030 emissions are expected to result in extreme hot years every second year by 2030 in twice as many countries (92%) as without their influence (46%). If all world nations shared the same fossil CO2 per capita emissions as projected for the US from 2016–2030, global warming in 2030 would be 0.4 °C higher than under actual current pledges, and 75% of all countries would exceed 2 °C of regional warming instead of 11%. Our results highlight the responsibility of individual emitters in driving regional climate change and provide additional angles for the climate policy discourse

MESMER - A Modular Earth System Model Emulator with Spatially Resolved Output

Humankind's carbon intensive lifestyle has been altering Earth's climate rapidly and today's policy decisions determine the amount of warming that is yet to occur. To understand the consequences of choices associated with different future emission pathways, as well as adaptation needs, targeted local-scale climate information accounting for major sources of climate change uncertainty is urgently needed. While Earth System Models (ESMs) are the most established tools to quantify the implications of changing greenhouse gas concentrations on the climate system, their computational cost implies that they can only sparsely explore regional climate change uncertainty. Computationally efficient ESM emulators are promising tools to fill the gap between the climate information that is needed and the climate information that can be provided by ESMs. In this thesis, a framework for regional ESM emulation is presented and its first implementation, a Modular Earth System Model Emulator with spatially Resolved output called MESMER, is developed. In combination with a global mean temperature emulator, MESMER is able to account for all three major sources of climate change projection uncertainty at the local scale: (i) internal variability uncertainty, i.e., unforced natural climate variability; (ii) forced climate response uncertainty, i.e., the Earth system's response to forced natural changes (solar and volcanic) and human influences (greenhouse gas and aerosol emissions, land use changes etc); and (iii) emission uncertainty, i.e., uncertainty in the emission pathway humans choose to follow. In four scientific studies, constituting individual chapters of this thesis, MESMER is introduced, extended, and applied in various contexts. In Chapter 2, the MESMER framework is proposed and used to successfully approximate annual temperature change field time series for the highest emission scenario of the Coupled Model Intercomparison Project phase 5 (CMIP5). MESMER is an ESM-specific emulator and, by being trained on each ESM individually, captures the different ESMs' unique characteristics. Thereby, only a single training simulation covering the historical period and the future scenario is required to calibrate MESMER for each ESM. This feature is of key importance to be able to emulate the full regional climate change uncertainty space spanned by the CMIP5 ensemble, since the majority of the CMIP5 ESMs only carried out a single simulation per emission scenario. By approximating the ESM-specific forced warming response as a direct function of forced global mean warming and by stochastically generating additional natural variability realizations for each CMIP5 ESM, an emulated multi-ESM initial-condition ensemble with thousands of members is created at a negligible computational cost. In Chapter 3, MESMER is employed to derive regionally-optimized projections of the highest emission scenario of the newly available Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. When evaluating individual ESMs' performance with respect to a global metric (global temperature trend) and a regional metric (regional response to global warming), it is revealed that there is no direct relationship between global and regional response performance. This motivates to "crossbreed" ESMs by recombining observationally-constrained global and local features of different ESMs. The resulting hybrid emulations thoroughly sample the observationally-consistent climate change projection uncertainty phase space. Differences between the emulated optimized projections and emulations of the original CMIP6 ensemble are spatially diverse but the highest warming projections are generally reduced. In Chapter 4, MESMER is coupled to the emission-driven, global mean emulator MAGICC. This MAGICC-MESMER emulator chain is able to provide ESM-specific as well as constrained probabilistic temperature change field time series for any given emission scenario. Here, MESMER is trained on all CMIP6 simulations from all available scenarios for each ESM to obtain as robust parameter estimates as possible. The resulting parameters are scenario-independent and it is confirmed that global forced warming is the only predictor needed to successfully emulate annual forced warming for a broad range of emission scenarios for most ESMs and in most regions. The emulated MAGICC-MESMER ensemble can complement and extend the original CMIP6 ensemble in three key aspects. (i) The forced climate response and natural variability uncertainty can be sampled more thoroughly with millions of emulations readily available. (ii) The same uncertainty space can be covered for any emission scenario, which is especially important because in the CMIP6 ensemble some of the societally most relevant high mitigation scenarios have been run by considerably fewer ESMs than other scenarios. (iii) Other lines of evidence, such as observational constraints, can be integrated to refine future projections. In Chapter 5, the MAGICC-MESMER emulator chain is used to assign responsibility for past and near-term future country-level warming and extreme hot years to single major emitters, a task which is too computationally expensive to be addressed with full ESM ensembles. Specifically, the contributions of the five largest historical emitters - China, the United States, the European Union, India, and Russia - are quantified in the context of their historical past and pledged future emissions until 2030. This is done both for actual emissions and for hypothetical per capita scenarios in which the whole globe's per capita emissions are set to follow each one of these emitters' per capita emissions. The results highlight the major emitters' relevance for regional-scale climate change and provide new angles for the climate policy discourse. MESMER, when used in combination with a global emulator, is the first tool to rapidly provide targeted local-scale climate information accounting for all major sources of climate change projection uncertainty. MESMER's code has been made publicly available to increase its accessibility and to accelerate its uptake by a broader user community. This opens perspectives for unprecedented near-real-time assessments of regionally resolved climate consequences induced by different policy choices for diverse users

Emulating Earth System Model land temperature fields with MESMER

Earth System Models (ESMs) are invaluable tools to study the climate system’s response to greenhouse gas emissions. But their projections are affected by three major sources of uncertainty: (i) internal variability, i.e., natural climate variability, (ii) ESM structural uncertainty, i.e., uncertainty in the response of the climate system to given greenhouse gas concentrations, and (iii) emission scenario uncertainty, i.e., which emission pathway the world chooses. The large computational cost of running full ESMs limits the exploration of this uncertainty phase space since it is only feasible to create a limited number of ESM runs. However, climate change impact and integrated assessment models, which require ESM projections as their input, could profit from a more complete sampling of the climate change uncertainty phase space. In this contribution, we present MESMER (Beusch et al., 2020), a Modular ESM Emulator with spatially Resolved output, which allows for a computationally efficient exploration of the uncertainty space of yearly temperatures. MESMER approximates ESM land temperature fields at a negligible computational cost by expressing grid-point-level temperatures as a function of global mean temperature and an overlaid spatio-temporally correlated variability term. Within MESMER all three major sources of uncertainty can be accounted for. Stochastic simulation of natural climate variability allows to account for internal variability. ESM structural uncertainty can be addressed by calibrating MESMER on different ESMs from the Coupled Model Intercomparison Project (CMIP) archives. Finally, emission scenario uncertainty can be accounted for by ingesting forced global mean temperature trajectories from global climate model emulators, such as MAGICC or FaIR. MESMER is a flexible statistical tool which is under active development and in the process of becoming an open-source software

Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections

The newest generation of the Coupled Model Intercomparison Project (CMIP6) exhibits a larger spread in temperature projections at the end of the 21st century than the previous generation. Here, a modular Earth System Model emulator is used to evaluate the realism of the warming signal in CMIP6 models on both global and regional scales, by comparing their global trends and regional response parameters to observations. Subsequently, the emulator is employed to derive large “crossbred” multimodel initial‐condition ensembles of regionally optimized land temperature projections by combining observationally constrained global mean temperature trend trajectories with observationally constrained local parameters. In the optimized ensembles, the warmest temperature projections are generally reduced and for the coolest projections both higher and lower values are found, depending on the region. The median shows less changes in large parts of the globe. These regional differences highlight the importance of a geographically explicit evaluation of Earth System Model projections.ISSN:0094-8276ISSN:1944-800

Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land

Earth system models (ESMs) are invaluable tools to study the climate system's response to specific greenhouse gas emission pathways. Large single-model initial-condition and multi-model ensembles are used to investigate the range of possible responses and serve as input to climate impact and integrated assessment models. Thereby, climate signal uncertainty is propagated along the uncertainty chain and its effect on interactions between humans and the Earth system can be quantified. However, generating both single-model initial-condition and multi-model ensembles is computationally expensive. In this study, we assess the feasibility of geographically explicit climate model emulation, i.e., of statistically producing large ensembles of land temperature field time series that closely resemble ESM runs at a negligible computational cost. For this purpose, we develop a modular emulation framework which consists of (i) a global mean temperature module, (ii) a local temperature response module, and (iii) a local residual temperature variability module. Based on this framework, MESMER, a Modular Earth System Model Emulator with spatially Resolved output, is built. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature from 1870 to 2100 on grid-point to regional scales with MESMER, it is sufficient to train on a single ESM run, but separate emulators need to be calibrated for individual ESMs given fundamental inter-model differences. We then emulate 40 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create a “superensemble”, i.e., a large ensemble which closely resembles a multi-model initial-condition ensemble. The thereby emerging ESM-specific emulator parameters provide essential insights on inter-model differences across a broad range of scales and characterize core properties of each ESM. Our results highlight that, for temperature at the spatiotemporal scales considered here, it is likely more advantageous to invest computational resources into generating multi-model ensembles rather than large single-model initial-condition ensembles. Such multi-model ensembles can be extended to superensembles with emulators like the one presented here.ISSN:2190-4987ISSN:2190-497

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

In this study, we develop and compare satellite rainfall retrievals based on generalized linear models and artificial neural networks. Both approaches are used in classification mode in a first step to identify the precipitating areas (precipitation detection) and in regression mode in a second step to estimate the rainfall intensity at the ground (rain rate). The input predictors are geostationary satellite infrared (IR) brightness temperatures and Satellite Application Facility (SAF) nowcasting products which consist of cloud properties, such as cloud top height and cloud type. Additionally, a set of auxiliary location-describing input variables is employed. The output predictand is the ground-based instantaneous rain rate provided by the European-scale radar composite OPERA, that was additionally quality-controlled. We compare our results to a precipitation product which uses a single infrared (IR) channel for the rainfall retrieval. Specifically, we choose the operational PR-OBS-3 hydrology SAF product as a representative example for this type of approach. With generalized linear models, we show that we are able to substantially improve in terms of hits by considering more IR channels and cloud property predictors. Furthermore, we demonstrate the added value of using artificial neural networks to further improve prediction skill by additionally reducing false alarms. In the rain rate estimation, the indirect relationship between surface rain rates and the cloud properties measurable with geostationary satellites limit the skill of all models, which leads to smooth predictions close to the mean rainfall intensity. Probability matching is explored as a tool to recover higher order statistics to obtain a more realistic rain rate distribution

Dynamics of a Puelche foehn event in the Andes

In this numerical modelling study, we investigate a Puelche foehn event (25–26 March 2014) in the southern Andes – a region with sparse observations. The synoptic environment as well as the mesoscale structure and the dynamics of the easterly wind are examined with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and a simulation with the mesoscale non-hydrostatic limited-area weather prediction model COSMO with a grid spacing of 2.2 km.The large-scale synoptic situation leading to this Puelche event is characterized by a mid-tropospheric cut-off low above the mountain range, the formation of a coastal surface low, as well as high pressure extending over the southern Andes. Easterly winds extend throughout the entire troposphere, indicative of a deep foehn flow. In the free troposphere, the easterlies are geostrophically balanced and develop in association with increasing pressure to the south. In contrast, within the planetary boundary layer, the easterly winds occur predominantly due to an increasing cross-range large-scale pressure gradient with only a weak geostrophic component. Kinematic trajectories indicate that a significant part of the Puelche air mass originates from above an inversion on the upstream side of the Andes. Some air parcels, however, ascend on the upstream side to crest height as the boundary layer deepens during daytime and/or flow through gaps across the mountain range. Hence, this Puelche event shares characteristics of both a blocked and a non-blocked foehn type.ISSN:0941-2948ISSN:1610-122

Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes.ISSN:0094-8276ISSN:1944-800

MESMER-M: an Earth system model emulator for spatially resolved monthly temperature

The degree of trust placed in climate model projections is commensurate with how well their uncertainty can be quantified, particularly at timescales relevant to climate policy makers. On inter-annual to decadal timescales, model projection uncertainty due to natural variability dominates at the local level and is imperative to describing near-term and seasonal climate events but difficult to quantify owing to the computational constraints of producing large ensembles. To this extent, emulators are valuable tools for approximating climate model runs, allowing for the exploration of the uncertainty space surrounding selected climate variables at a substantially reduced computational cost. Most emulators, however, operate at annual to seasonal timescales, leaving out monthly information that may be essential to assessing climate impacts. This study extends the framework of an existing spatially resolved, annual-scale Earth system model (ESM) emulator (MESMER, Beusch et al., 2020) by a monthly downscaling module (MESMER-M), thus providing local monthly temperatures from local yearly temperatures. We first linearly represent the mean response of the monthly temperature cycle to yearly temperatures using a simple harmonic model, thus maintaining month-to-month correlations and capturing changes in intra-annual variability. We then construct a month-specific local variability module which generates spatiotemporally correlated residuals with yearly temperature- and month-dependent skewness incorporated within. The emulator's ability to capture the yearly temperature-induced monthly temperature response and its surrounding uncertainty due to natural variability is demonstrated for 38 different ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The emulator is furthermore benchmarked using a simple gradient-boosting-regressor-based model trained on biophysical information. We find that while regional-scale, biophysical feedbacks may induce non-uniformities in the yearly to monthly temperature downscaling relationship, statistical emulation of regional effects shows comparable skill to the more physically informed approach. Thus, MESMER-M is able to statistically generate ESM-like, large initial-condition ensembles of spatially explicit monthly temperature fields, providing monthly temperature probability distributions which are of critical value to impact assessments.ISSN:2190-4987ISSN:2190-497