René Orth

Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture‐temperature feedbacks

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 multi-model 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 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.

Role of soil moisture versus recent climate change for the 2010 heat wave in western Russia

The severe 2010 heat wave in western Russia was found to be influenced by anthropogenic climate change. Additionally, soil moisture-temperature feedbacks were deemed important for the buildup of the exceptionally high temperatures. We quantify the relative role of both factors by applying the probabilistic event attribution framework and analyze ensemble simulations to distinguish the effect of climate change and the 2010 soil moisture conditions for annual maximum temperatures. The dry 2010 soil moisture alone has increased the risk of a severe heat wave in western Russia sixfold, while climate change from 1960 to 2000 has approximately tripled it. The combined effect of climate change and 2010 soil moisture yields a 13 times higher heat wave risk. We conclude that internal climate variability causing the dry 2010 soil moisture conditions formed a necessary basis for the extreme heat wave.

Record dry summer in 2015 challenges precipitation projections in Central Europe

Central Europe was characterized by a humid-temperate climate in the 20th century. Climate change projections suggest that climate in this area will shift towards warmer temperatures by the end of the 21st century, while projected precipitation changes are highly uncertain. Here we show that the 2015 summer rainfall was the lowest on record since 1901 in Central Europe, and that climate models that perform best in the three driest years of the historical time period 1901–2015 project stronger drying trends in the 21st century than models that perform best in the remaining years. Analyses of precipitation and derived soil moisture reveal that the 2015 event was drier than both the recent 2003 or 2010 extreme summers in Central Europe. Additionally there are large anomalies in satellite-derived vegetation greenness. In terms of precipitation and temperature anomalies, the 2015 summer in Central Europe is found to lie between historical climate in the region and that characteristic of the Mediterranean area. Even though the models best capturing past droughts are not necessarily generally more reliable in the future, the 2015 drought event illustrates that potential future drying trends have severe implications and could be stronger than commonly assumed from the entire IPCC AR5 model ensemble.

A submonthly database for detecting changes in vegetation-atmosphere coupling

Land-atmosphere coupling and changes in coupling regimes are important for making precise future climate predictions and understanding vegetation-climate feedbacks. Here we introduce the Vegetation-Atmosphere Coupling (VAC) index which identifies regions and times of concurrent strong anomalies in temperature and photosynthetic activity. The different classes of the index determine whether a location is currently in an energy-limited or water-limited regime, and its high temporal resolution allows to investigate how these regimes change over time at the regional scale. We show that the VAC index helps to distinguish different evaporative regimes. It can therefore provide indirect information about the local soil moisture state. We further demonstrate how the index can be used to understand processes leading to and occurring during extreme climate events, using the 2010 heat wave in Russia and the 2010 Amazon drought as examples.

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

AbstractSoil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. The authors investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Their approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information...

Introduction of a simple-model-based land surface dataset for Europe

Land surface hydrology can play a crucial role during extreme events such as droughts, floods and even heat waves. We introduce in this study a new hydrological dataset for Europe that consists of soil moisture, runoff and evapotranspiration (ET). It is derived with a simple water balance model (SWBM) forced with precipitation, temperature and net radiation. The SWBM dataset extends over the period 1984–2013 with a daily time step and 0.5° × 0.5° resolution. We employ a novel calibration approach, in which we consider 300 random parameter sets chosen from an observation-based range. Using several independent validation datasets representing soil moisture (or terrestrial water content), ET and streamflow, we identify the best performing parameter set and hence the new dataset. To illustrate its usefulness, the SWBM dataset is compared against several state-of-the-art datasets (ERA-Interim/Land, MERRA-Land, GLDAS-2-Noah, simulations of the Community Land Model Version 4), using all validation datasets as reference. For soil moisture dynamics it outperforms the benchmarks. Therefore the SWBM soil moisture dataset constitutes a reasonable alternative to sparse measurements, little validated model results, or proxy data such as precipitation indices. Also in terms of runoff the SWBM dataset performs well, whereas the evaluation of the SWBM ET dataset is overall satisfactory, but the dynamics are less well captured for this variable. This highlights the limitations of the dataset, as it is based on a simple model that uses uniform parameter values. Hence some processes impacting ET dynamics may not be captured, and quality issues may occur in regions with complex terrain. Even though the SWBM is well calibrated, it cannot replace more sophisticated models; but as their calibration is a complex task the present dataset may serve as a benchmark in future. In addition we investigate the sources of skill of the SWBM dataset and find that the parameter set has a similar impact on the simple model results as the choice of the forcing dataset. The newly derived SWBM dataset is of relevance for a range of applications given the deficit of available land datasets. It is available for download on www.iac.ethz.ch/url/SWBM-Dataset.

Variability of Soil Moisture and Sea Surface Temperatures Similarly Important for Warm-Season Land Climate in the Community Earth System Model

AbstractBoth sea surface temperatures (SSTs) and soil moisture (SM) can influence climate over land. This paper presents a comprehensive comparison of SM versus SST impacts on land climate in the warm season. The authors perform fully coupled ensemble experiments with the Community Earth System Model in which they prescribe SM or SSTs to the long-term median seasonal cycles. It is found that SM variability overall impacts warm-season land climate to a similar extent as SST variability, in the midlatitudes, tropics, and subtropics. Removing SM or SST variability impacts land climate means and reduces land climate variability at different time scales by 10%–50% (temperature) and 0%–10% (precipitation). Both SM- and SST-induced changes are strongest for hot temperatures (up to 50%) and for extreme precipitation (up to 20%). These results are qualitatively similar for the present day and the end of the twenty-first century. Removed SM variability affects surface climate through corresponding variations in sur...

Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty

AbstractThe land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model.Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the po...

Methods and model dependency of extreme event attribution: The 2015 European drought

Science on the role of anthropogenic influence on extreme weather events, such as heatwaves or droughts, has evolved rapidly in the past years. The approach of “event attribution” compares the occurrence-probability of an event in the present, factual, climate with its probability in a hypothetical, counterfactual, climate without human-induced climate change. Several methods can be used for event attribution, based on climate model simulations and observations, and usually researchers only assess a subset of methods and data sources. Here, we explore the role of methodological choices for the attribution of the 2015 meteorological summer drought in Europe. We present contradicting conclusions on the relevance of human influence as a function of the chosen data source and event attribution methodology. Assessments using the maximum number of models and counterfactual climates with pre-industrial greenhouse gas concentrations point to an enhanced drought risk in Europe. However, other evaluations show contradictory evidence. These results highlight the need for a multi-model and multi-method framework in event attribution research, especially for events with a low signal-to-noise ratio and high model dependency such as regional droughts.

Did European temperatures in 1540 exceed present-day records?

There is strong evidence that the year 1540 was exceptionally dry and warm in Central Europe. Here we infer 1540 summer temperatures from the number of dry days (NDDs) in spring (March–May) and summer (June–August) in 1540 derived from historical documentary evidence published elsewhere, and compare our estimates with present-day temperatures. We translate the NDD values into temperature distributions using a linear relationship between modeled temperature and NDD from a 3000 year pre-industrial control simulation with the Community Earth System Model (CESM). Our results show medium confidence that summer mean temperatures (T JJA) and maximum temperatures (TXx) in Central Europe in 1540 were warmer than the respective present-day mean summer temperatures (assessed between 1966–2015). The model-based reconstruction suggests further that with a probability of 40%–70%, the highest daily temperatures in 1540 were even warmer than in 2003, while there is at most a 20% probability that the 1540 mean summer temperature was warmer than that of 2003 in Central Europe. As with other state-of-the-art analyses, the uncertainty of the reconstructed 1540 summer weather in this study is considerable, for instance as extrapolation is required because 1540-like events are not captured by the employed Earth system model (ESM), and neither by other ESMs. However, in addition to paleoclimatological approaches we introduce here an independent methodology to estimate 1540 temperatures, and contribute consequently to a reduced overall uncertainty in the analysis of this event. The characterization of such events and the related climate system functioning is particularly relevant in the context of global warming and the corresponding increase of extreme heat wave magnitude and occurrence frequency.