Ruth Lorenz

Last Name

Lorenz

First Name

Ruth

ORCID

0000-0002-3986-1268

Organisational unit

02240 - Center for Climate Systems Modeling / Center for Climate Systems Modeling

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Publications1 - 10 of 21

ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing
Abramowitz, Gab; Herger, Nadja; Gutmann, Ethan; et al. (2019)
Earth System Dynamics
The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates; therefore, a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near-duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognised by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.
Taking climate model evaluation to the next level
Eyring, Veronika; Cox, Peter M.; Flato, Gregory M.; et al. (2019)
Nature Climate Change
Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.
A climate model projection weighting scheme accounting for performance and interdependence
Knutti, Reto; Sedláček, Jan; Sanderson, Benjamin M.; et al. (2017)
Geophysical Research Letters
Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.
Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America
Lorenz, Ruth; Herger, Nadja; Sedláček, Jan; et al. (2018)
Journal of Geophysical Research: Atmospheres
Uncertainties in climate projections exist due to natural variability, scenario uncertainty, and model uncertainty. It has been argued that model uncertainty can be decreased by giving more weight to those models in multimodel ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multimodel ensembles are not independent. We use a weighting approach proposed recently that takes into account both model performance and interdependence and apply it to investigate projections of summer maximum temperature climatology over North America in two regions of different sizes. We quantify the influence of predicting diagnostics included in the method, look at ways how to choose them, and assess the influence of the observational data set used. The trend in shortwave radiation, mean precipitation, sea surface temperature variability, and variability and trend in maximum temperature itself are the most promising constraints on projections of summer maximum temperature over North America. The influence of the observational data sets is large for summer temperature climatology, since the observational and reanalysis products used for absolute maximum temperatures disagree. Including multiple predicting diagnostics leads to more similar results for different data sets. We find that the weighted multimodel mean reduces the change in summer daily temperature maxima compared to the nonweighted mean slightly (0.05–0.45 °C) over the central United States. We show that it is essential to have reliable observations for key variables to be able to constrain multimodel ensembles of future projections.
Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks
Vogel, Martha M.; Orth, Rene; Chéruy, Frédérique; et al. (2017)
Geophysical Research Letters
Regional hot extremes are projected to increase more strongly than global mean temperature, with substantially larger changes than 2°C even if global warming is limited to this level. We investigate the role of soil moisture-temperature feedbacks for this response based on multimodel experiments for the 21st century with either interactive or fixed (late 20th century mean seasonal cycle) soil moisture. We analyze changes in the hottest days in each year in both sets of experiments, relate them to the global mean temperature increase, and investigate processes leading to these changes. We find that soil moisture-temperature feedbacks significantly contribute to the amplified warming of the hottest days compared to that of global mean temperature. This contribution reaches more than 70% in Central Europe and Central North America. Soil moisture trends are more important for this response than short-term soil moisture variability. These results are relevant for reducing uncertainties in regional temperature projections.
How Important is Vegetation Phenology for European Climate and Heat Waves?
Lorenz, Ruth; Davin, Edouard Léopold; Lawrence, David M.; et al. (2013)
Journal of Climate
Influence of soil moisture and vegetation phenology on recent European heat waves
Lorenz, Ruth (2012)
Modeling land-climate coupling in Europe: Impact of land surface representation on climate variability and extremes
Lorenz, Ruth; Davin, Edouard Léopold; Seneviratne, Sonia I. (2012)
Journal of Geophysical Research: Atmospheres
Land-climate coupling has been shown to be important for European summer climate variability and extreme events. However, the sensitivity of these feedbacks to land surface model (LSM) choice has been little investigated up to now. In this study, we assess the impact of the LSM on the simulated climate variability in a regional climate model (RCM). The experiments were conducted with the COSMO-CLM2RCM. COSMO-CLM²can be run with two alternative LSMs, the 2nd-generation LSM TERRA_ML or the more sophisticated 3rd-generation LSM Community Land Model (CLM3.5). The analyzed simulations include control and sensitivity experiments with prescribed soil moisture (dry or wet). Using CLM3.5 instead of TERRA_ML improves the simulated temperature variability by alleviating an overestimation of temperature inter-annual variability in the RCM. Also, the representation of the probability density functions of daily maximum summer temperature is improved when using the more advanced LSM. The reduced climate variability is linked to a larger ground heat flux and smaller variability in soil moisture and short-wave radiation. The latter effect results from the coupling of the LSM to the atmospheric module. In addition, using CLM3.5 reduces the sensitivity of COSMO-CLM²to extreme soil moisture conditions. An analysis assessing the relationship between the standard precipitation index and the subsequent number of hot days in summer reveals a better representation of this relationship using CLM3.5. Hence, we find that biases in climate variability and extremes can be reduced and the representation of land-climate coupling can be improved with the use of the more sophisticated LSM.
Detection of a Climate Change Signal in Extreme Heat, Heat Stress, and Cold in Europe From Observations
Lorenz, Ruth; Stalhandske, Zélie; Fischer, Erich M. (2019)
Geophysical Research Letters
In the last two decades Europe experienced a series of high‐impact heat extremes. We here assess observed trends in temperature extremes at ECA&D stations in Europe. We demonstrate that on average across Europe the number of days with extreme heat and heat stress has more than tripled and hot extremes have warmed by 2.3 °C from 1950–2018. Over Central Europe, the warming exceeds the corresponding summer mean warming by 50%. Days with extreme cold temperatures have decreased by a factor of 2–3 and warmed by more than 3 °C, regionally substantially more than winter mean temperatures. Cold and hot extremes have warmed at about 94% of stations, a climate change signal that cannot be explained by internal variability. The clearest climate change signal can be detected in maximum heat stress. EURO‐CORDEX RCMs broadly capture observed trends but the majority underestimates the warming of hot extremes and overestimates the warming of cold extremes.
Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity
Zhou, Sha; Williams, A. Park; Berg, Alexis M.; et al. (2019)
Proceedings of the National Academy of Sciences of the United States of America
Soil drought and atmospheric aridity can be disastrous for ecosystems and society. This study demonstrates the critical role of land–atmosphere feedbacks in driving cooccurring soil drought and atmospheric aridity. The frequency and intensity of atmospheric aridity are greatly reduced without the feedback of soil moisture to atmospheric temperature and humidity. Soil moisture can also impact precipitation to amplify soil moisture deficits under dry conditions. These land–atmosphere processes lead to high probability of concurrent soil drought and atmospheric aridity. Compared to the historical period, models project future frequency and intensity of concurrent soil drought and atmospheric aridity to be further enhanced by land–atmosphere feedbacks, which may pose large risks to ecosystem services and human well-being in the future.

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Ruth Lorenz

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