Abstract. The first simulation experiment and output archives of the Project to Intercompare Regional Climate Simulations (PIRCS) is described. Initial results from simulations of the summer 1988 drought over the central United States indicate that limited-area models forced by large-scale information at the lateral boundaries reproduce bulk temporal and spatial characteristics of meteorological fields. In particular, the 500 hPa height field time average and temporal variability are generally well simulated by all participating models. Model simulations of precipitation episodes vary depending on the scale of the dynamical forcing. Organized synoptic-scale precipitation systems are simulated deterministically in that precipitation occurs at close to the same time and location as observed (although amounts may vary from observations). Episodes of mesoscale and convective precipitation are represented in a more stochastic sense, with less precise agreement in temporal and spatial patterns. Simulated surface energy fluxes show broad similarity with the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) observations in their temporal evolution and time average diurnal cycle. Intermodel differences in midday Bowen ratio tend to be closely associated with precipitation differences. Differences in daily maximum temperatures also are linked to Bowen ratio differences, indicating strong local, surface influence on this field. Although some models have bias with respect to FIFE observations, all tend to reproduce the synoptic variability of observed daily maximum and minimum temperatures. Results also reveal the advantage of an intercomparison in exposing common tendencies of models despite their differences in convective and surface parameterizations and different methods of assimilating lateral boundary conditions.

State-of-the-art socioeconomic scenarios of land-cover change in the Amazon basin for the years 2030 and 2050 are used together with the Regional Atmospheric Modeling System (RAMS) to simulate the hydrometeorological changes caused by deforestation in that region under diverse climatological conditions that include both El Niño and La Niña events. The basin-averaged rainfall progressively decreases with the increase of deforestation from 2000 to 2030, 2050, and so on, to total deforestation by the end of the twenty-first century. Furthermore, the spatial distribution of rainfall is significantly affected by both the land-cover type and topography. While the massively deforested region experiences an important decrease of precipitation, the areas at the edge of that region and at elevated regions receive more rainfall. Propagating squall lines over the massively deforested region dissipate before reaching the western part of the basin, causing a significant decrease of rainfall that could result in a catastrophic collapse of the ecosystem in that region. The basin experiences much stronger precipitation changes during El Niño events as deforestation increases. During these periods, deforestation in the western part of the basin induces a very significant decrease of precipitation. During wet years, however, deforestation has a minor overall impact on the basin climatology.

A series of numerical simulations were performed to evaluate the capability of the Regional Atmospheric Modeling System (RAMS) to simulate the evolution of convection in a partly deforested region of the Amazon basin during the rainy season, and to elucidate some of the complex land–atmosphere interactions taking place in that region. Overall, it is demonstrated that RAMS can simulate properly the domain-average accumulated rainfall in Rondônia, Brazil, when provided with reliable initial profiles of atmospheric relative humidity and soil moisture. It is also capable of simulating important feedbacks involving the energy partition at the ground surface and the formation of convection. In general, more water in the soil and/or the atmosphere produces more rainfall. However, these conditions affect the onset of rainfall in opposite ways; while higher atmospheric relative humidity leads to early rainfall, higher soil moisture delays its formation. As compared to stratiform clouds, which tend to cover a large area, convective clouds are localized and they let relatively more solar radiation reach the ground surface. As a result, a stronger sensible heat flux is released at the ground surface, which enhances the atmospheric instability and reinforces convection. Simulations using horizontal grid elements 2 and 4 km in size show a delay and decrease of rainfall as compared to simulations with high-resolution grids whose elements are not larger than 1 km and, as a result, afflict RAMS performance. It is concluded that RAMS can be used as a reliable tool to simulate the various hydrometeorological processes involved in land-cover changes as a result of deforestation in this region.

[1] Using a spatially-explicit model, we have projected potential Amazon landscapes based on two possible development scenarios and total forest removal to represent uncertainty in future land cover. We conducted Monte Carlo simulations with a regional climate model driven by these landscapes and by different years to include atmospheric uncertainty. Absent restraints on development, we find that certain areas can expect annual rainfall declines of 3 -5% that persist in spite of introduced uncertainty. These declines are strongly tied to key landscape features. Land cover and land use change associated with major roads, not ENSO events or other annual atmospheric features, leads to reduced rainfall. For the case of total deforestation we found an average annual decline in rainfall of 10-20% across the entire basin.

Cloud streets are common feature in the Amazon Basin. They form from the combination of the vertical trade wind stress and moist convection. Here, satellite imagery, data collected during the COBRA-PARÁ (Caxiuanã Observations in the Biosphere, River and Atmosphere of Pará) field campaign, and high resolution modeling are used to understand the streets 0 formation and behavior. The observations show that the streets have an aspect ratio of about 3.5 and they reach their maximum activity around 15:00 UTC when the wind shear is weaker, and the convective boundary layer reaches its maximum height. The simulations reveal that the cloud streets onset is caused by the local circulations and convection produced at the interfaces between forest and rivers of the Amazon. The satellite data and modeling show that the large rivers anchor the cloud streets producing a quasi-stationary horizontal pattern. The streets are associated with horizontal roll vortices parallel to the mean flow that organizes the turbulence causing advection of latent heat flux towards the upward branches. The streets have multiple warm plumes that promote a connection between the rolls. These spatial patterns allow fundamental insights on the interpretation of the Amazon exchanges between surface and atmosphere with important consequences for the climate change understanding.