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

Impact of soil map specifications for European climate simulations

Soil physical characteristics can influence terrestrial hydrology and the energy balance and may thus affect land–atmosphere exchanges. However, only few studies have investigated the importance of soil textures for climate. In this study, we examine the impact of soil texture specification in a regional climate model. We perform climate simulations over Europe using soil maps derived from two different sources: the soil map of the world from the Food and Agricultural Organization and the European Soil Database from the European Commission Joint Research Center. These simulations highlight the importance of the specified soil texture in summer, with differences of up to 2 °C in mean 2-m temperature and 20 % in precipitation resulting from changes in the partitioning of energy at the land surface into sensible and latent heat flux. Furthermore, we perform additional simulations where individual soil parameters are perturbed in order to understand their role for summer climate. These simulations highlight the importance of the vertical profile of soil moisture for evapotranspiration. Parameters affecting the latter are hydraulic diffusivity parameters, field capacity and plant wilting point. Our study highlights the importance of soil properties for climate simulations. Given the uncertainty associated with the geographical distribution of soil texture and the resulting differences between maps from different sources, efforts to improve existing databases are needed. In addition, climate models would benefit from tackling unresolved issues in land-surface modeling related to the high spatial variability in soil parameters, both horizontally and vertically, and to limitations of the concept of soil textural class

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

Attributing human mortality during extreme heat waves to anthropogenic climate change

It has been argued that climate change is the biggest global health threat of the 21st century. The extreme high temperatures of the summer of 2003 were associated with up to seventy thousand excess deaths across Europe. Previous studies have attributed the meteorological event to the human influence on climate, or examined the role of heat waves on human health. Here, for the first time, we explicitly quantify the role of human activity on climate and heat-related mortality in an event attribution framework, analysing both the Europe-wide temperature response in 2003, and localised responses over London and Paris. Using publicly-donated computing, we perform many thousands of climate simulations of a high-resolution regional climate model. This allows generation of a comprehensive statistical description of the 2003 event and the role of human influence within it, using the results as input to a health impact assessment model of human mortality. We find large-scale dynamical modes of atmospheric variability remain largely unchanged under anthropogenic climate change, and hence the direct thermodynamical response is mainly responsible for the increased mortality. In summer 2003, anthropogenic climate change increased the risk of heat-related mortality in Central Paris by ~70% and by ~20% in London, which experienced lower extreme heat. Out of the estimated ~315 and ~735 summer deaths attributed to the heatwave event in Greater London and Central Paris, respectively, 64 (±3) deaths were attributable to anthropogenic climate change in London, and 506 (±51) in Paris. Such an ability to robustly attribute specific damages to anthropogenic drivers of increased extreme heat can inform societal responses to, and responsibilities for, climate change

Climate signals in river flood damages emerge under sound regional disaggregation

Climate change affects precipitation patterns. Here, we investigate whether its signals are already detectable in reported river flood damages. We develop an empirical model to reconstruct observed damages and quantify the contributions of climate and socio-economic drivers to observed trends. We show that, on the level of nine world regions, trends in damages are dominated by increasing exposure and modulated by changes in vulnerability, while climate-induced trends are comparably small and mostly statistically insignificant, with the exception of South & Sub-Saharan Africa and Eastern Asia. However, when disaggregating the world regions into subregions based on river-basins with homogenous historical discharge trends, climate contributions to damages become statistically significant globally, in Asia and Latin America. In most regions, we find monotonous climate-induced damage trends but more years of observations would be needed to distinguish between the impacts of anthropogenic climate forcing and multidecadal oscillations

Land–Atmosphere Interactions: The LoCo Perspective

Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

Land-Atmosphere Interactions: The LoCo Perspective

Land-atmosphere (L-A) interactions are a main driver of Earth's surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land-Atmosphere System Study (GLASS) panel has supported 'L-A coupling' as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hotspots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local L-A Coupling ('LoCo') project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales, and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

Identifying Key Driving Processes of Major Recent Heat Waves

Heat waves lead to major impacts on human health, food production, and ecosystems. To assess their predictability and how they are projected to change under global warming, it is crucial to improve our understanding of the underlying processes affecting their occurrence and intensity under present‐day climate conditions. Beside greenhouse gas forcing, processes in the different components of the climate system—in particular the land surface, atmospheric circulation, and the oceans—may play a key role in changing the odds for a particular event. This study aims to identify the role of the individual drivers for five heat waves (and, in some cases, of concurrent droughts) in the recent decade. Simulations are performed with the Community Earth System Model using nudging of horizontal atmospheric circulation and prescription of soil moisture. The fully constrained model accurately reproduces how anomalous an event was. Factorial experiments, which force the model toward observations for one or several key components at a time, allow us to identify how much of the observed temperature anomaly of each event can be attributed to each driver. Considering all analyzed events, atmospheric circulation and soil moisture play similarly important roles, each contributing between 20% and 70% to the events' anomalies. This highlights that the role of thermodynamics can be just as important as that of the dynamics for temperature extremes, a possibly underestimated feature. In addition, recent climate change amplified the events and contributed between 10% and 40% of the events' anomalies.ISSN:0148-0227ISSN:2169-897

Intercomparison of daily precipitation persistence in multiple global observations and climate models

Daily precipitation persistence is affected by various atmospheric and land processes and provides complementary information to precipitation amount statistics for understanding the precipitation dynamics. In this study, daily precipitation persistence is assessed in an exhaustive ensemble of observation-based daily precipitation datasets and evaluated in global climate model (GCM) simulations for the period of 2001-2013. Daily precipitation time series are first transformed into categorical time series of dry and wet spells with a 1 mm d(-1) precipitation threshold. Subsequently, P-dd (P-ww), defined as the probability of a dry (wet) day to be followed by another dry (wet) day is calculated to represent daily precipitation persistence. The analysis focuses on the long-term mean and interannual variability (IAV) of the two indices. Both multi-observation and multi-model means show higher values of P-dd than P-ww. GCMs overestimate P-ww with a relatively homogeneous spatial bias pattern. They overestimate P-dd in the Amazon and Central Africa but underestimate P-dd in several regions such as southern Argentina, western North America and the Tibetan Plateau. The IAV of both P-dd and P-ww is generally underestimated in climate models, but more strongly for P-ww. Overall, our results highlight systematic model errors in daily precipitation persistence that are substantially larger than the already considerable spread across observational products. These findings also provide insights on how precipitation persistence biases on a daily time scale relate to well-documented persistence biases at longer time scales in state-of-the-art GCMs