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)

Land-atmospheric feedbacks during droughts and heatwaves : state of the science and current challenges

Droughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity, especially when they occur simultaneously as combined events. Their predicted increase in recurrence and intensity poses serious threats to future food security. Still today, the knowledge of how droughts and heatwaves start and evolve remains limited, and so does our understanding of how climate change may affect them. Droughts and heatwaves have been suggested to intensify and propagate via land-atmosphere feedbacks. However, a global capacity to observe these processes is still lacking, and climate and forecast models are immature when it comes to representing the influences of land on temperature and rainfall. Key open questions remain in our goal to uncover the real importance of these feedbacks: What is the impact of the extreme meteorological conditions on ecosystem evaporation? How do these anomalies regulate the atmospheric boundary layer state (event self-intensification) and contribute to the inflow of heat and moisture to other regions (event self-propagation)? Can this knowledge on the role of land feedbacks, when available, be exploited to develop geo-engineering mitigation strategies that prevent these events from aggravating during their early stages? The goal of our perspective is not to present a convincing answer to these questions, but to assess the scientific progress to date, while highlighting new and innovative avenues to keep advancing our understanding in the future

Constraining future terrestrial carbon cycle projections using observation‐based water and carbon flux estimates

The terrestrial biosphere is currently acting as a sink for about a third of the total anthropogenic CO2 emissions. However, the future fate of this sink in the coming decades is very uncertain, as current earth system models (ESMs) simulate diverging responses of the terrestrial carbon cycle to upcoming climate change. Here, we use observation-based constraints of water and carbon fluxes to reduce uncertainties in the projected terrestrial carbon cycle response derived from simulations of ESMs conducted as part of the 5th phase of the Coupled Model Intercomparison Project (CMIP5). We find in the ESMs a clear linear relationship between present-day evapotranspiration (ET) and gross primary productivity (GPP), as well as between these present-day fluxes and projected changes in GPP, thus providing an emergent constraint on projected GPP. Constraining the ESMs based on their ability to simulate present-day ET and GPP leads to a substantial decrease in the projected GPP and to a ca. 50% reduction in the associated model spread in GPP by the end of the century. Given the strong correlation between projected changes in GPP and in NBP in the ESMs, applying the constraints on net biome productivity (NBP) reduces the model spread in the projected land sink by more than 30% by 2100. Moreover, the projected decline in the land sink is at least doubled in the constrained ensembles and the probability that the terrestrial biosphere is turned into a net carbon source by the end of the century is strongly increased. This indicates that the decline in the future land carbon uptake might be stronger than previously thought, which would have important implications for the rate of increase in the atmospheric CO2 concentration and for future climate change

Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation

The extent of the Amazon rainforest is projected to drastically decrease in future decades because of land-use changes. Previous climate modelling studies have found that the biogeophysical effects of future Amazonian deforestation will likely increase surface temperatures and reduce precipitation locally. However, the magnitude of these changes and the potential existence of tipping points in the underlying relationships is still highly uncertain. Using a regional climate model at a resolution of about 50 km over the South American continent, we perform four ERA-interim-driven simulations with prescribed land cover maps corresponding to present-day vegetation, two deforestation scenarios for the twenty-first century, and a totally-deforested Amazon case. In response to projected land cover changes for 2100, we find an annual mean surface temperature increase of 0.5∘C over the Amazonian region and an annual mean decrease in rainfall of 0.17 mm/day compared to present-day conditions. These estimates reach 0.8∘C and 0.22 mm/day in the total-deforestation case. We also compare our results to those from 28 previous (regional and global) climate modelling experiments. We show that the historical development of climate models did not modify the median estimate of the Amazonian climate sensitivity to deforestation, but led to a reduction of its uncertainty. Our results suggest that the biogeophysical effects of deforestation alone are unlikely to lead to a tipping point in the evolution of the regional climate under present-day climate conditions. However, the conducted synthesis of the literature reveals that this behaviour may be model-dependent, and the greenhouse gas-induced climate forcing and biogeochemical feedbacks should also be taken into account to fully assess the future climate of this region

Reconciling spatial and temporal soil moisture effects on afternoon rainfall

Soil moisture impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to soil moisture heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous soil moisture conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of moisture recycling with local, spatially negative feedbacks

Reconciling spatial and temporal soil moisture effects on afternoon rainfall

Soil moisture impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to soil moisture heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous soil moisture conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of moisture recycling with local, spatially negative feedbacks