Soil Moisture Effects on Afternoon Precipitation Occurrence in Current Climate Models

Soil moisture precipitation feedbacks in the Eastern European Alpine region in convection-permitting climate simulations

A novel convection permitting modelling framework that combines a pseudo-global warming approach with continuously forced deep soil moisture from prescribed perturbation storylines is applied in the Eastern European Alpine region and parts of the Pannonian Basin to investigate soil moisture precipitation (SMP) feedbacks on summertime precipitation and the feedbacks' role under changed climate conditions. A set of 1-year convection-permitting (3 km horizontal grid spacing) soil moisture sensitivity simulations with the regional climate model of the Consortium for Small-Scale Modelling in Climate Mode are conducted. In order to account for global warming, end-of-the-century climate change effects from four global climate models, projecting the greenhouse gas concentration scenario RCP 8.5, are imprinted. The simulations reveal that (1) the locations of precipitation events are highly sensitive to soil moisture modifications while intensities and the internal structure of precipitation events are nearly unaffected and (2) high precipitation intensities are more likely in combinations with positive temporal but distinctive (either strong positive or strong negative) spatial SMP coupling. Low precipitation intensities are in favour of combinations of negative temporal and positive spatial coupling. The analyses suggest that soil moisture at a given time acts as a guiding field for the location of the next precipitation event. Interestingly, this behaviour is independent of climate change, although the coupling strength's increase is 1.5-1.7 times larger than expected from linear climate change scaling when climate becomes 50% dryer. Finally, it is found that (1) local deviations in the climate change signal of summertime precipitation in the range of up to ±40% are caused by uncertainty in deep soil moisture in the range of ±10% and (2) these local deviations in the climate change signal are dominated by soil moisture uncertainty in future climate conditions.

Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data

Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects of floods, drought, and landslides. However, they are collected frequently from the scarce and often insufficient network of ground-based rain-gauge stations to generate continuous precipitation maps. Recently, precipitation maps derived from satellite data have not been sufficiently linked to ground-based rain gauges and satellite-derived soil moisture to improve the assessment of precipitation distribution using spatial statistics. Kriging methods are used to enhance the estimation of the spatial distribution of precipitations. The aim of this study was to assess two geostatistical methods, ordinary kriging (OK) and ordinary cokriging (OCK), and one deterministic method (i.e., inverse distance weighting (IDW)) for improved spatial interpolation of quarterly and monthly precipitations in Poland and near-border areas of the neighbouring countries (~325,000 or 800,000 km2). Quarterly precipitation data collected during a 5-year period (2010–2014) from 113–116 rain-gauge stations located in the study area were used. Additionally, monthly precipitations in the years 2014–2017 from over 400 rain-gauge stations located in Poland were used. The spatiotemporal data on soil moisture (SM) from the Soil Moisture and Ocean Salinity (SMOS) global satellite (launched in 2009) were used as an auxiliary variable in addition to precipitation for the OCK method. The predictive performance of the spatial distribution of precipitations was the best for OCK for all quarters, as indicated by the coefficient of determination (R2 = 0.944–0.992), and was less efficient (R2 = 0.039–0.634) for the OK and IDW methods. As for monthly precipitation, the performance of OCK was considerably higher than that of IDW and OK, similarly as with quarterly precipitation. The performance of all interpolation methods was better for monthly than for quarterly precipitations. The study indicates that SMOS data can be a valuable source of auxiliary data in the cokriging and/or other multivariate methods for better estimation of the spatial distribution of precipitations in various regions of the world.

A novel method for assessing climate change impacts in ecotron experiments

Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change.

Advances in understanding large-scale responses of the water cycle to climate change

Globally, thermodynamics explains an increase in atmospheric water vapor with warming of around 7%/°C near to the surface. In contrast, global precipitation and evaporation are constrained by the Earth's energy balance to increase at ∼2-3%/°C. However, this rate of increase is suppressed by rapid atmospheric adjustments in response to greenhouse gases and absorbing aerosols that directly alter the atmospheric energy budget. Rapid adjustments to forcings, cooling effects from scattering aerosol, and observational uncertainty can explain why observed global precipitation responses are currently difficult to detect but are expected to emerge and accelerate as warming increases and aerosol forcing diminishes. Precipitation increases with warming are expected to be smaller over land than ocean due to limitations on moisture convergence, exacerbated by feedbacks and affected by rapid adjustments. Thermodynamic increases in atmospheric moisture fluxes amplify wet and dry events, driving an intensification of precipitation extremes. The rate of intensification can deviate from a simple thermodynamic response due to in-storm and larger-scale feedback processes, while changes in large-scale dynamics and catchment characteristics further modulate the frequency of flooding in response to precipitation increases. Changes in atmospheric circulation in response to radiative forcing and evolving surface temperature patterns are capable of dominating water cycle changes in some regions. Moreover, the direct impact of human activities on the water cycle through water abstraction, irrigation, and land use change is already a significant component of regional water cycle change and is expected to further increase in importance as water demand grows with global population.