Intra-annual link of spring and autumn precipitation over France

In a previous study, an intra-annual relationship of observed precipitation, manifested by negative correlations between domain-averaged spring and autumn precipitation of the same year, was found in two domains covering France and Central Europe for the period 1972-1990 (Hirschi etal., J Geophys Res 112(D22109), 2007). Here, this link and its temporal evolution over France during the twentieth century is further investigated and related to the atmospheric circulation and North Atlantic/Mediterranean sea surface temperature (SST) patterns. Observational datasets of precipitation, mean sea level pressure (MSLP), atmospheric teleconnection patterns, and SST, as well as various global and regional climate model simulations are analyzed. The investigation of observed precipitation by means of a running correlation with a 30-year time window for the period 1901-present reveals a decreasing trend in the spring-to-autumn correlations, which become significantly negative in the second half of the twentieth century. These negative correlations are connected with similar spring-to-autumn correlations in observed MSLP, and with negatively correlated spring East Atlantic (EA) and autumn Scandinavian (SCA) teleconnection pattern indices. Maximum covariance analyses of SST with these atmospheric variables indicate that at least part of the identified spring-to-autumn link is mediated through SST, as spring precipitation and MSLP are connected with the same autumn SST pattern as are autumn precipitation, MSLP and the SCA pattern index. Except for ERA-40 driven regional climate models from the EU-FP6 project ENSEMBLES, the analyzed regional and global climate models, including Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations, do not capture this observed variability in precipitation. This is associated with the failure of most models in simulating the observed correlations between spring and autumn MSLP. While the causes for the identified relationship cannot be fully established its timing suggests a possible link with increased aerosol loading in the global dimming perio

Global changes in extreme events: regional and seasonal dimension

This study systematically analyzes the complete IPCC AR4 (CMIP3) ensemble of GCM simulations with respect to changes in extreme event characteristics at the end of the 21st century compared to present-day conditions. It complements previous studies by investigating a more comprehensive database and considering seasonal changes beside the annual time scale. Confirming previous studies, the agreement between the GCMs is generally high for temperature-related extremes, indicating increases of warm day occurrences and heatwave lengths, and decreases of cold extremes. However, we identify issues with the choice of indices used to quantify heatwave lengths, which do overall not affect the sign of the changes, but strongly impact the magnitude and patterns of projected changes in heatwave characteristics. Projected changes in precipitation and dryness extremes are more ambiguous than those in temperature extremes, despite some robust features, such as increasing dryness over the Mediterranean and increasing heavy precipitation over the Northern high latitudes. We also find that the assessment of projected changes in dryness depends on the index choice, and that models show less agreement regarding changes in soil moisture than in the commonly used ‘consecutive dry days' index, which is based on precipitation data only. Finally an analysis of the scaling of changes of extreme temperature quantiles with global, regional and seasonal warming shows that much of the extreme quantile changes are due to a seasonal scaling of the regional annual-mean warming. This emphasizes the importance of the seasonal time scale also for extremes. Changes in extreme quantiles of temperature on land scale with changes in global annual mean temperature by a factor of more than 2 in some regions and seasons, implying large changes in extremes in several countries, even for the commonly discussed global 2°C-warming targe

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

Remotely sensed Earth observations have many missing values. The abundance and often complex patterns of these missing values can be a barrier for combining different observational datasets and may cause biased estimates of derived statistics. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatial or temporal interpolation or by upscaling approaches in which complete donor variables are used to infer missing values. However, these approaches typically do not account for information that may be present in other observed variables that also have missing values. Here we propose CLIMFILL (CLIMate data gap-FILL), a multivariate gap-filling procedure that combines kriging interpolation with a statistical gap-filling method designed to account for the dependence across multiple gappy variables. In a first stage, an initial gap fill is constructed for each variable separately using state-of-the-art spatial interpolation. Subsequently, the initial gap fill for each variable is updated to recover the dependence across variables using an iterative procedure. Estimates for missing values are thus informed by knowledge of neighbouring observations, temporal processes, and dependent observations of other relevant variables. CLIMFILL is tested using gap-free ERA-5 reanalysis data of ground temperature, surface-layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These variables were matched with corresponding remote sensing observations and masked where the observations have missing values. In this “perfect dataset approach” CLIMFILL can be evaluated against the original, usually not observed part of the data. We show that CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations in the gap-filled dataset. Correlation between original ERA-5 data and gap-filled ERA-5 data is high in many regions, although it shows artefacts of the interpolation procedure in large gaps in high-latitude regions during winter. Bias and noise in gappy satellite-observable data is reduced in most regions. A case study of the European 2003 heatwave shows how CLIMFILL reduces biases in ground temperature and surface-layer soil moisture induced by the missing values. Furthermore, in idealized experiments we see the impact of fraction of missing values and the complexity of missing value patterns to the performance of CLIMFILL, showing that CLIMFILL for most variables operates at the upper limit of what is possible given the high fraction of missing values and the complexity of missingness patterns. Thus, the framework can be a tool for gap filling a large range of remote sensing observations commonly used in climate and environmental research.</p

Climate engineering of vegetated land for hot extremes mitigation: An Earth system model sensitivity study

Various climate engineering schemes have been proposed as a way to curb anthropogenic climate change. Land climate engineering schemes aiming to reduce the amount of solar radiation absorbed at the surface by changes in land surface albedo have been considered in a limited number of investigations. However, global studies on this topic have generally focused on the impacts on mean climate rather than extremes. Here we present the results of a series of transient global climate engineering sensitivity experiments performed with the Community Earth System Model over the time period 1950–2100 under historical and Representative Concentration Pathway 8.5 scenarios. Four sets of experiments are performed in which the surface albedo over snow-free vegetated grid points is increased respectively by 0.05, 0.10, 0.15, and 0.20. The simulations show a preferential cooling of hot extremes relative to mean temperatures throughout the Northern midlatitudes during boreal summer under the late twentieth century conditions. Two main mechanisms drive this response: On the one hand, a stronger efficacy of the albedo-induced radiative forcing on days with high incoming shortwave radiation and, on the other hand, enhanced soil moisture-induced evaporative cooling during the warmest days relative to the control simulation due to accumulated soil moisture storage and reduced drying. The latter effect is dominant in summer in midlatitude regions and also implies a reduction of summer drought conditions. It thus constitutes another important benefit of surface albedo modifications in reducing climate change impacts. The simulated response for the end of the 21st century conditions is of the same sign as that for the end of the twentieth century conditions but indicates an increasing absolute impact of land surface albedo increases in reducing mean and extreme temperatures under enhanced greenhouse gas forcing

Historical Land-Cover Change Impacts on Climate: Comparative Assessment of LUCID and CMIP5 Multimodel Experiments

During the industrial period, many regions experienced a reduction in forest cover and an expansion of agricultural areas, in particular North America, northern Eurasia, and South Asia. Here, results from the Land-Use and Climate, Identification of Robust Impacts (LUCID) and CMIP5 model intercomparison projects are compared in order to investigate how land-cover changes (LCC) in these regions have locally impacted the biophysical land surface properties, like albedo and evapotranspiration, and how this has affected seasonal mean temperature as well as its diurnal cycle. The impact of LCC is extracted from climate simulations, including all historical forcings, using a method that is shown to capture well the sign and the seasonal cycle of the impacts diagnosed from single-forcing experiments in most cases. The model comparison reveals that both the LUCID and CMIP5 models agree on the albedo-induced reduction of mean winter temperatures over midlatitudes. In contrast, there is less agreement concerning the response of the latent heat flux and, subsequently, mean temperature during summer, when evaporative cooling plays a more important role. Overall, a majority of models exhibit a local warming effect of LCC during this season, contrasting with results from the LUCID studies. A striking result is that none of the analyzed models reproduce well the changes in the diurnal cycle identified in present-day observations of the effect of deforestation. However, overall the CMIP5 models better simulate the observed summer daytime warming effect compared to the LUCID models, as well as the winter nighttime cooling effect

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

Soil 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. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our 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 on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography

GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Freshwater resources are of high societal relevance, and understanding their past variability is vital to water management in the context of ongoing climate change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In situ streamflow observations are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis. The accuracy of this reconstruction is assessed with cross-validation and compared with an independent set of discharge observations for large river basins. The presented dataset agrees on average better with the streamflow observations than an ensemble of 13 state-of-the art global hydrological model runoff simulations. We estimate a global long-term mean runoff of 38 452 km³ yr⁻¹ in agreement with previous assessments. The temporal coverage of the reconstruction offers an unprecedented view on large-scale features of runoff variability in regions with limited data coverage, making it an ideal candidate for large-scale hydro-climatic process studies, water resource assessments, and evaluating and refining existing hydrological models. The paper closes with example applications fostering the understanding of global freshwater dynamics, interannual variability, drought propagation and the response of runoff to atmospheric teleconnections. The GRUN dataset is available at https://doi.org/10.6084/m9.figshare.9228176 (Ghiggi et al., 2019)

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results